3d:I[70291,["/_next/static/immutable/chunks/3byybp2bb3w2h.js","/_next/static/immutable/chunks/0mquf-m7kztqq.js","/_next/static/immutable/chunks/18s3gqaar5p-u.js","/_next/static/immutable/chunks/0uthazyo52-8r.js","/_next/static/immutable/chunks/3wl6rhmu93bz2.js","/_next/static/immutable/chunks/3ahbe8k0rpasp.js","/_next/static/immutable/chunks/30vmsnxjx8eqt.js"],"PersonalizedJobList"] 3e:T10ed, About CodeRoad CodeRoad provides end-to-end software development services, helping businesses scale with ideal infrastructure solutions. From staff augmentation to dedicated IT teams and general software engineering, our nearshore technology services empower businesses to thrive in an ever-evolving digital landscape. About t he Role As the Lead Agentic AI Engineer , you will serve as the primary technical authority responsible for architecting, engineering, and operationalizing enterprise-grade autonomous agent systems integrated directly with the Salesforce platform. You will lead the implementation of stateful multi-agent workflows, leveraging frontier LLMs (with a strong emphasis on the Anthropic Claude ecosystem), Model Context Protocol ( MCP ), and modern orchestration engines to bridge complex business requirements with robust software engineering. This role is critical to transforming enterprise business processes by embedding safe, high-accuracy, and deterministic autonomous AI agents into Salesforce CRM, Data Cloud, and core operational systems. You will anchor the vision, evaluation pipelines, and architectural standards that allow high-throughput agentic workflows to operate securely within strict transactional boundaries and enterprise data security protocols. Key Responsibilities Architect stateful multi-agent coordination graphs using LangGraph or custom DAG loops, supporting dynamic task decomposition, supervisor orchestration, human-in-the-loop escalation, and automated error recovery. Build standardized MCP (Model Context Protocol) servers and connectors to securely expose Salesforce tools, objects, custom actions, and operational context to agent runners. Optimize query routes and context engineering across model tiers (such as Claude Sonnet/Opus for multi-turn reasoning vs. Haiku for high-throughput utility tasks) to optimize latency, accuracy, and token spend. Design automated evaluation harnesses ( Promptfoo , Ragas , or DeepEval ) integrated into CI/CD pipelines to continuously benchmark grounding, schema validity, and hallucination resistance against reference datasets. Implement full-stack observability and defense-in-depth security using tools like LangSmith or Langfuse alongside strict input sanitization, field-level security ( FLS ), and automated PII scrubbing. Lead technical delivery standards, establishing API interface contracts, specification-driven development patterns, and architectural blueprints for cross-functional engineering teams and executive stakeholders. Requirements 7+ years of hands-on enterprise software engineering experience using Python , TypeScript/Node.js , Java , or Apex . 3+ years of direct production experience building, evaluating, and scaling LLM-powered applications and autonomous multi-agent workflows. Tech Stack: Expert proficiency with LangGraph / LangChain , Anthropic/Claude APIs (tool calling, prompt caching, Claude Code), MCP , and observability tools ( LangSmith , Langfuse , or OpenTelemetry ). Evaluation Frameworks: Hands-on experience with automated LLM eval suites ( Promptfoo , Ragas , or LLM-as-a-judge patterns) as automated test gates. Ownership mindset with deep diagnostic debugging skills for isolating non-deterministic LLM behavior, context degradation, and latency bottlenecks. Consultative Communication: Strong ability to translate complex AI mechanics into actionable architecture roadmaps for technical and non-technical stakeholders. Advanced English: Fluent written and spoken professional English for direct client consultation. Nice to Have Practical integration experience with Salesforce APIs (REST, Composite, Bulk), SOQL/SOSL , Platform Events, and Connected Apps. Familiarity with Salesforce Data Cloud , vector search capabilities, or Agentforce architectural patterns. Experience deploying containerized MCP servers integrated with enterprise CRM security boundaries. Hands-on exposure to Spec-Driven Development (SDD) methodologies for AI-assisted software delivery workflows. What You’ll Love 100% Remote work flexibility National holidays off Generous Paid Time Off (PTO) Health insurance assistance Competitive compensation in USD Continuous learning and professional growth opportunities 3f:T242f,

About CodeRoad

CodeRoad provides end-to-end software development services, helping businesses scale with ideal infrastructure solutions. From staff augmentation to dedicated IT teams and general software engineering, our nearshore technology services empower businesses to thrive in an ever-evolving digital landscape.

About the Role

As the Lead Agentic AI Engineer, you will serve as the primary technical authority responsible for architecting, engineering, and operationalizing enterprise-grade autonomous agent systems integrated directly with the Salesforce platform. You will lead the implementation of stateful multi-agent workflows, leveraging frontier LLMs (with a strong emphasis on the Anthropic Claude ecosystem), Model Context Protocol (MCP), and modern orchestration engines to bridge complex business requirements with robust software engineering.

This role is critical to transforming enterprise business processes by embedding safe, high-accuracy, and deterministic autonomous AI agents into Salesforce CRM, Data Cloud, and core operational systems. You will anchor the vision, evaluation pipelines, and architectural standards that allow high-throughput agentic workflows to operate securely within strict transactional boundaries and enterprise data security protocols.

Key Responsibilities

Architect stateful multi-agent coordination graphs using LangGraph or custom DAG loops, supporting dynamic task decomposition, supervisor orchestration, human-in-the-loop escalation, and automated error recovery.
Build standardized MCP (Model Context Protocol) servers and connectors to securely expose Salesforce tools, objects, custom actions, and operational context to agent runners.
Optimize query routes and context engineering across model tiers (such as Claude Sonnet/Opus for multi-turn reasoning vs. Haiku for high-throughput utility tasks) to optimize latency, accuracy, and token spend.
Design automated evaluation harnesses (Promptfoo, Ragas, or DeepEval) integrated into CI/CD pipelines to continuously benchmark grounding, schema validity, and hallucination resistance against reference datasets.
Implement full-stack observability and defense-in-depth security using tools like LangSmith or Langfuse alongside strict input sanitization, field-level security (FLS), and automated PII scrubbing.
Lead technical delivery standards, establishing API interface contracts, specification-driven development patterns, and architectural blueprints for cross-functional engineering teams and executive stakeholders.

Requirements

7+ years of hands-on enterprise software engineering experience using Python, TypeScript/Node.js, Java, or Apex.
3+ years of direct production experience building, evaluating, and scaling LLM-powered applications and autonomous multi-agent workflows.
Tech Stack: Expert proficiency with LangGraph / LangChain, Anthropic/Claude APIs (tool calling, prompt caching, Claude Code), MCP, and observability tools (LangSmith, Langfuse, or OpenTelemetry).
Evaluation Frameworks: Hands-on experience with automated LLM eval suites (Promptfoo, Ragas, or LLM-as-a-judge patterns) as automated test gates.
Ownership mindset with deep diagnostic debugging skills for isolating non-deterministic LLM behavior, context degradation, and latency bottlenecks.
Consultative Communication: Strong ability to translate complex AI mechanics into actionable architecture roadmaps for technical and non-technical stakeholders.
Advanced English: Fluent written and spoken professional English for direct client consultation.

Nice to Have

Practical integration experience with Salesforce APIs (REST, Composite, Bulk), SOQL/SOSL, Platform Events, and Connected Apps.
Familiarity with Salesforce Data Cloud, vector search capabilities, or Agentforce architectural patterns.
Experience deploying containerized MCP servers integrated with enterprise CRM security boundaries.
Hands-on exposure to Spec-Driven Development (SDD) methodologies for AI-assisted software delivery workflows.

What You’ll Love

100% Remote work flexibility
National holidays off
Generous Paid Time Off (PTO)
Health insurance assistance
Competitive compensation in USD
Continuous learning and professional growth opportunities

40:T217a,Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale — unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the precision of search and the intelligence of AI to enable everyone to accelerate the results that matter. By taking advantage of all structured and unstructured data — securing and protecting private information more effectively — Elastic’s complete, cloud-based solutions for search, security, and observability help organizations deliver on the promise of AI. What is The Role The Context Engine team builds the knowledge layer that AI agents use to work with enterprise data in Elasticsearch. We extract knowledge from any data sources into a structured AI Index, serve it to agents through public APIs, MCP tools and framework integrations, and close the loop with agent traces so that what the engine knows improves from real usage. Any agent can use it: Elastic’s own Agent Builder, Claude Code, LangChain and other third-party harnesses. As a Principal AI Engineer, you own the improvement loop of this product end to end: how agents, automations and skills behave in production, how we observe them, how we evaluate them, and how we ship changes to them safely. This is a hybrid role at the intersection of engineering, data science, and product. You will write production code, design evaluation and telemetry that product decisions can rest on, and set the technical bar for how the team iterates on agentic behaviour. You will work alongside data scientists, backend engineers, product, and UX, and your work will show up directly in what customers build on top of Elastic. The codebase is TypeScript and we build it in the open, so you'll be shipping code, designs and discussions in public alongside the rest of the Elastic Stack. What You Will Be Doing Own the production improvement loop for Context Engine: understand how extraction automations, retrieval tools and memory behave, based on offline evaluations and customer conversations and telemetry. You help find the failure modes, fix them, and prove the fix. Define how we iterate on agents and skills safely: versioning and rollout of prompts, skills and automations, regression coverage, staged and shadow evaluation, and the guardrails that let us change behaviour without breaking customers. Design the telemetry we need to make data-informed engineering decisions: what to capture from agent traces, tool calls and knowledge retrieval, how it lands in Elasticsearch, and how it feeds evaluation, dashboards and the feedback loop. Partner with the data science team on evaluation strategy: golden datasets, evaluators to gate on quality, latency and cost. Raise the bar across the team: review designs and PRs, mentor engineers in eval-driven development, and write the technical proposals that shape the roadmap. What You Bring 10+ years of software engineering experience, with the recent years spent shipping and operating AI-driven products on real production traffic, ideally products with public APIs and data models that had to evolve without breaking customers. A track record of eval-driven product improvement: you have diagnosed agent or LLM behaviour from traces and user feedback, designed the evaluation that exposed the problem, shipped the fix and measured the outcome. Direct experience building agents with state and memory, and iterating on prompts, skills and tool behaviour safely in production. Familiarity with MCP, including exposing public MCP servers and tools. Experience designing telemetry for AI systems, and using it to make engineering and product decisions. Experience running product experiments end to end: instrumentation, unattended execution, and interpreting results. Experience building products with public APIs and evolving data models, and the judgement that comes with maintaining external contracts as a product changes. Strong backend engineering skills in either Python or TypeScript: APIs, stateful workflows, data pipelines and production services. Comfort working with data scientists, engineers, and product managers as peers, translating between measurement and shipping, and communicating trade-offs clearly. A pragmatic, low-ego style suited to a distributed, async-first team. Bonus Points Experience with agent frameworks such as LangGraph, CrewAI, Claude Agent SDK, or similar. Agentic retrieval experience, including knowledge representation. Practical Elasticsearch experience. Compensation for this role is in the form of base salary. This role does not have a variable compensation component. At Elastic, our compensation philosophy aims to provide fair, competitive and transparent remuneration. Salary ranges are established based on a combination of external market benchmarks, internal pay equity considerations, and the responsibilities and complexity associated with each role. This approach helps ensure consistency across comparable roles while remaining competitive within the relevant labour markets. The final compensation offered within the applicable range will be determined based on several objective factors, including relevant professional experience, level of skills and expertise, alignment with the role requirements, and the overall scope and complexity of the position. The typical starting salary range for this role is: 369 400 zł — 584 300 zł PLN Additional Information - We Take Care of Our People As a distributed company, diversity drives our identity. Whether you’re looking to launch a new career or grow an existing one, Elastic is the type of company where you can balance great work with great life. Your age is only a number. It doesn’t matter if you’re just out of college or your children are; we need you for what you can do. We strive to have parity of benefits across regions and while regulations differ from place to place, we believe taking care of our people is the right thing to do. Competitive pay based on the work you do here and not your previous salary Health coverage for you and your family in many locations Ability to craft your calendar with flexible locations and schedules for many roles Generous number of vacation days each year Increase your impact - We match up to $2000 (or local currency equivalent) for financial donations and service Up to 40 hours each year to use toward volunteer projects you love Embracing parenthood with minimum of 16 weeks of parental leave Different people approach problems differently. We need that. Elastic is an equal opportunity employer and is committed to creating an inclusive culture that celebrates different perspectives, experiences, and backgrounds. Qualified applicants will receive consideration for employment without regard to race, ethnicity, color, religion, sex, pregnancy, sexual orientation, gender perception or identity, national origin, age, marital status, protected veteran status, disability status, or any other basis protected by federal, state or local law, ordinance or regulation. We welcome individuals with disabilities and strive to create an accessible and inclusive experience for all individuals. To request an accommodation during the application or the recruiting process, please email candidate_accessibility@elastic.co . We will reply to your request within 24 business hours of submission. Applicants have rights under Federal Employment Laws, view posters linked below: Family and Medical Leave Act (FMLA) Poster; Pay Transparency Nondiscrimination Provision Poster; Employee Polygraph Protection Act (EPPA) Poster and Know Your Rights (Poster) Elasticsearch develops and distributes technology and information that is subject to U.S. and other countries’ export controls and licensing requirements for individuals who are located in or are nationals of the following sanctioned countries and regions: Belarus, Cuba, Iran, North Korea, Syria, or Russia, including the Ukrainian territories annexed by Russia (The Crimea region of Ukraine, The Donetsk People's Republic (DNR), The Luhansk People's Republic (LNR), Kherson or Zaporizhzhia). If you are located in or are a national of one of the listed countries or regions, an export license may be required as a condition of your employment in this role. Please note that national origin and/or nationality do not affect eligibility for employment with Elastic. Please see here for our Privacy Statement.41:T2851,

Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale — unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the precision of search and the intelligence of AI to enable everyone to accelerate the results that matter. By taking advantage of all structured and unstructured data — securing and protecting private information more effectively — Elastic’s complete, cloud-based solutions for search, security, and observability help organizations deliver on the promise of AI.

What is The Role

The Context Engine team builds the knowledge layer that AI agents use to work with enterprise data in Elasticsearch. We extract knowledge from any data sources into a structured AI Index, serve it to agents through public APIs, MCP tools and framework integrations, and close the loop with agent traces so that what the engine knows improves from real usage. Any agent can use it: Elastic’s own Agent Builder, Claude Code, LangChain and other third-party harnesses.

As a Principal AI Engineer, you own the improvement loop of this product end to end: how agents, automations and skills behave in production, how we observe them, how we evaluate them, and how we ship changes to them safely. This is a hybrid role at the intersection of engineering, data science, and product. You will write production code, design evaluation and telemetry that product decisions can rest on, and set the technical bar for how the team iterates on agentic behaviour. You will work alongside data scientists, backend engineers, product, and UX, and your work will show up directly in what customers build on top of Elastic.

The codebase is TypeScript and we build it in the open, so you'll be shipping code, designs and discussions in public alongside the rest of the Elastic Stack.

What You Will Be Doing

Own the production improvement loop for Context Engine: understand how extraction automations, retrieval tools and memory behave, based on offline evaluations and customer conversations and telemetry. You help find the failure modes, fix them, and prove the fix.
Define how we iterate on agents and skills safely: versioning and rollout of prompts, skills and automations, regression coverage, staged and shadow evaluation, and the guardrails that let us change behaviour without breaking customers.
Design the telemetry we need to make data-informed engineering decisions: what to capture from agent traces, tool calls and knowledge retrieval, how it lands in Elasticsearch, and how it feeds evaluation, dashboards and the feedback loop.
Partner with the data science team on evaluation strategy: golden datasets, evaluators to gate on quality, latency and cost.
Raise the bar across the team: review designs and PRs, mentor engineers in eval-driven development, and write the technical proposals that shape the roadmap.

What You Bring

10+ years of software engineering experience, with the recent years spent shipping and operating AI-driven products on real production traffic, ideally products with public APIs and data models that had to evolve without breaking customers.
A track record of eval-driven product improvement: you have diagnosed agent or LLM behaviour from traces and user feedback, designed the evaluation that exposed the problem, shipped the fix and measured the outcome.
Direct experience building agents with state and memory, and iterating on prompts, skills and tool behaviour safely in production.
Familiarity with MCP, including exposing public MCP servers and tools.
Experience designing telemetry for AI systems, and using it to make engineering and product decisions.
Experience running product experiments end to end: instrumentation, unattended execution, and interpreting results.
Experience building products with public APIs and evolving data models, and the judgement that comes with maintaining external contracts as a product changes.
Strong backend engineering skills in either Python or TypeScript: APIs, stateful workflows, data pipelines and production services.
Comfort working with data scientists, engineers, and product managers as peers, translating between measurement and shipping, and communicating trade-offs clearly.
A pragmatic, low-ego style suited to a distributed, async-first team.

Bonus Points

Experience with agent frameworks such as LangGraph, CrewAI, Claude Agent SDK, or similar.
Agentic retrieval experience, including knowledge representation.
Practical Elasticsearch experience.

Compensation for this role is in the form of base salary. This role does not have a variable compensation component.

At Elastic, our compensation philosophy aims to provide fair, competitive and transparent remuneration. Salary ranges are established based on a combination of external market benchmarks, internal pay equity considerations, and the responsibilities and complexity associated with each role. This approach helps ensure consistency across comparable roles while remaining competitive within the relevant labour markets.

The final compensation offered within the applicable range will be determined based on several objective factors, including relevant professional experience, level of skills and expertise, alignment with the role requirements, and the overall scope and complexity of the position.

The typical starting salary range for this role is:

369 400 zł—584 300 zł PLN

Additional Information - We Take Care of Our People

As a distributed company, diversity drives our identity. Whether you’re looking to launch a new career or grow an existing one, Elastic is the type of company where you can balance great work with great life. Your age is only a number. It doesn’t matter if you’re just out of college or your children are; we need you for what you can do.

We strive to have parity of benefits across regions and while regulations differ from place to place, we believe taking care of our people is the right thing to do.

Competitive pay based on the work you do here and not your previous salary
Health coverage for you and your family in many locations
Ability to craft your calendar with flexible locations and schedules for many roles
Generous number of vacation days each year
Increase your impact - We match up to $2000 (or local currency equivalent) for financial donations and service
Up to 40 hours each year to use toward volunteer projects you love
Embracing parenthood with minimum of 16 weeks of parental leave

Different people approach problems differently. We need that. Elastic is an equal opportunity employer and is committed to creating an inclusive culture that celebrates different perspectives, experiences, and backgrounds. Qualified applicants will receive consideration for employment without regard to race, ethnicity, color, religion, sex, pregnancy, sexual orientation, gender perception or identity, national origin, age, marital status, protected veteran status, disability status, or any other basis protected by federal, state or local law, ordinance or regulation.

We welcome individuals with disabilities and strive to create an accessible and inclusive experience for all individuals. To request an accommodation during the application or the recruiting process, please email candidate_accessibility@elastic.co. We will reply to your request within 24 business hours of submission.

Applicants have rights under Federal Employment Laws, view posters linked below: Family and Medical Leave Act (FMLA) Poster; Pay Transparency Nondiscrimination Provision Poster; Employee Polygraph Protection Act (EPPA) Poster and Know Your Rights (Poster)

Elasticsearch develops and distributes technology and information that is subject to U.S. and other countries’ export controls and licensing requirements for individuals who are located in or are nationals of the following sanctioned countries and regions: Belarus, Cuba, Iran, North Korea, Syria, or Russia, including the Ukrainian territories annexed by Russia (The Crimea region of Ukraine, The Donetsk People's Republic (DNR), The Luhansk People's Republic (LNR), Kherson or Zaporizhzhia). If you are located in or are a national of one of the listed countries or regions, an export license may be required as a condition of your employment in this role. Please note that national origin and/or nationality do not affect eligibility for employment with Elastic.

Please see here for our Privacy Statement.

42:T18c0,About Fluidstack We exist to make humanity more free. For most of human history, you farmed or you starved. Technology gave people more time for the things they wanted to do, instead of things they had to do. Powerful AI will be the biggest lever for human choice we've ever built - but only if models are aligned with what humanity actually wants. There are groups building AI who don't share these goals. Whoever deploys frontier compute infrastructure fastest will decide whether AI expands human freedom or shrinks it. We're singularly focused on delivering 10 to 100s of GWs of compute faster than anyone else, rethinking every layer of the stack. We acquire power, design and build data centers, and operate them - with teams spanning hardware and software. Speed and scale are our key differentiators. Come be a part of building civilization-scale infrastructure for AI. We hire people who care deeply about this problem space. If that is you, please apply! How We Operate Be a barrel. Full autonomy. Own things end to end, take on scope without being asked, no permission required to operate outside your core role. Insane urgency. We drive everything forward as fast as possible. Reason from first principles. Challenge every assumption. Zero analogy thinking, no egos, the best idea wins. Love of the game. The frontier of AI is the most interesting problem of our time. We put in long hours at high intensity to push the frontier forward. Build something that actually matters. If you're going to spend your time, spend it on something that matters to the world. The Decision Team Examples of key problems the team is working on Automate the delivery of gigawatts. Every process that takes AI infrastructure from land to live compute becomes software: schedules, decisions, and todos generated from a live knowledge graph instead of chased by hand. Forward-deploy beside the experts. Product teams sit with quality managers, sourcing leads, and deployment engineers on factory floors and sites, and turn their judgment into systems that reach every unit. Deliver every supercomputer faster than the last. Dozens of concurrent projects feed one graph, so every lesson learned at one site becomes a preventive check at all of them. Role Scope Turn the bill of materials into the order book: every template bill carries the price and lead time of each line, every project bill locks against a building, and every purchase order derives from it, so tens of billions of dollars of GPUs, optics, switchgear, and cooling are ordered from one source of truth instead of a spreadsheet. Make procurement tracking trustworthy: the rules that decide when a line is late, what its status means, and which lines sit on the critical path run in code against live vendor emails, documents, and general contractor purchase orders through the extractors you ship, so drift is caught the week it happens and the recovery plan is drafted before the vendor calls. Close the loop from receiving to payment: every delivery matches a purchase order line, every warehouse bin knows its serial numbers, cycle counts are scored against an accuracy gate, and finance three-way matches from the same record, so the question "did we get what we paid for" has one answer instead of three. Ship manufacturing as software before the factory opens: manufacturing bills of materials, engineering change notices, and the drawing tree live in one model, inspection and test plans and first article procedures generate per asset from the design revision, and a failed check opens a nonconformance with the evidence attached and rescores the vendor. Embed with sourcing leads, quality managers, factory operators, and warehouse teams, on their floors and in their vendor calls, and turn their judgment into the systems that decide the order schedule, the cash demand by month, and the payment schedule the company plans against. What We're Looking For The below is a starting point. We always make space for exceptional people, so if you don't fit this role exactly, tell us where you would . You've shipped production code in Go, Python, or TypeScript, and you pick up whatever language the problem demands. You've built real features on LLM APIs (OpenAI, Anthropic, or open-weight models), MCP servers, and agentic frameworks, and you've put document extraction into production against messy real inputs. You work daily with AI coding tools like Claude Code and Cursor, and you get agents doing useful work autonomously alongside you. You identify problems, design the solution, and ship it without waiting for direction or approval. You've moved fast under deadline while leaving data models and foundations that other engineers extended after you moved on. You've earned credibility with operators in factories, warehouses, or procurement, and driven adoption of your software inside their real deadlines, like a live production run or a quarter-end close. Your product taste shows in what you've shipped: interfaces the floor calls obvious, and workflows that match how the work actually happens. Bonus: MES, ERP, WMS, or TMS systems. BOM lineage, engineering change control, and serialized component traceability. Supplier quality (NCRs, CAPA, FAI). Three-way match and receiving reconciliation. LLM extraction over vendor documents. High-volume hardware supply chains. Benefits: Competitive total compensation package (cash + equity) Health, dental, and vision insurance Retirement plan Generous PTO policy We are committed to pay equity and transparency. Fluidstack is an Equal Employment Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability and protected veterans’ status, or any other characteristic protected by law. Fluidstack will consider for employment qualified applicants with arrest and conviction records pursuant to applicable law. You will receive a confirmation email once your application has successfully been accepted. If there is an error with your submission and you did not receive a confirmation email, please email careers@fluidstack.io with your resume/CV, the role you've applied for, and the date you submitted your application-- someone from our recruiting team will be in touch.43:T2142,

About Fluidstack

We exist to make humanity more free. For most of human history, you farmed or you starved. Technology gave people more time for the things they wanted to do, instead of things they had to do. Powerful AI will be the biggest lever for human choice we've ever built - but only if models are aligned with what humanity actually wants. There are groups building AI who don't share these goals. Whoever deploys frontier compute infrastructure fastest will decide whether AI expands human freedom or shrinks it.

We're singularly focused on delivering 10 to 100s of GWs of compute faster than anyone else, rethinking every layer of the stack. We acquire power, design and build data centers, and operate them - with teams spanning hardware and software. Speed and scale are our key differentiators. Come be a part of building civilization-scale infrastructure for AI.

We hire people who care deeply about this problem space. If that is you, please apply!

How We Operate

Be a barrel. Full autonomy. Own things end to end, take on scope without being asked, no permission required to operate outside your core role.
Insane urgency. We drive everything forward as fast as possible.
Reason from first principles. Challenge every assumption. Zero analogy thinking, no egos, the best idea wins.
Love of the game. The frontier of AI is the most interesting problem of our time. We put in long hours at high intensity to push the frontier forward.
Build something that actually matters. If you're going to spend your time, spend it on something that matters to the world.

The Decision Team

Examples of key problems the team is working on

Automate the delivery of gigawatts. Every process that takes AI infrastructure from land to live compute becomes software: schedules, decisions, and todos generated from a live knowledge graph instead of chased by hand.
Forward-deploy beside the experts. Product teams sit with quality managers, sourcing leads, and deployment engineers on factory floors and sites, and turn their judgment into systems that reach every unit.
Deliver every supercomputer faster than the last. Dozens of concurrent projects feed one graph, so every lesson learned at one site becomes a preventive check at all of them.

Role Scope

Turn the bill of materials into the order book: every template bill carries the price and lead time of each line, every project bill locks against a building, and every purchase order derives from it, so tens of billions of dollars of GPUs, optics, switchgear, and cooling are ordered from one source of truth instead of a spreadsheet.
Make procurement tracking trustworthy: the rules that decide when a line is late, what its status means, and which lines sit on the critical path run in code against live vendor emails, documents, and general contractor purchase orders through the extractors you ship, so drift is caught the week it happens and the recovery plan is drafted before the vendor calls.
Close the loop from receiving to payment: every delivery matches a purchase order line, every warehouse bin knows its serial numbers, cycle counts are scored against an accuracy gate, and finance three-way matches from the same record, so the question "did we get what we paid for" has one answer instead of three.
Ship manufacturing as software before the factory opens: manufacturing bills of materials, engineering change notices, and the drawing tree live in one model, inspection and test plans and first article procedures generate per asset from the design revision, and a failed check opens a nonconformance with the evidence attached and rescores the vendor.
Embed with sourcing leads, quality managers, factory operators, and warehouse teams, on their floors and in their vendor calls, and turn their judgment into the systems that decide the order schedule, the cash demand by month, and the payment schedule the company plans against.

What We're Looking For

The below is a starting point. We always make space for exceptional people, so if you don't fit this role exactly, tell us where you would.

You've shipped production code in Go, Python, or TypeScript, and you pick up whatever language the problem demands.
You've built real features on LLM APIs (OpenAI, Anthropic, or open-weight models), MCP servers, and agentic frameworks, and you've put document extraction into production against messy real inputs.
You work daily with AI coding tools like Claude Code and Cursor, and you get agents doing useful work autonomously alongside you.
You identify problems, design the solution, and ship it without waiting for direction or approval.
You've moved fast under deadline while leaving data models and foundations that other engineers extended after you moved on.
You've earned credibility with operators in factories, warehouses, or procurement, and driven adoption of your software inside their real deadlines, like a live production run or a quarter-end close.
Your product taste shows in what you've shipped: interfaces the floor calls obvious, and workflows that match how the work actually happens.
Bonus: MES, ERP, WMS, or TMS systems. BOM lineage, engineering change control, and serialized component traceability. Supplier quality (NCRs, CAPA, FAI). Three-way match and receiving reconciliation. LLM extraction over vendor documents. High-volume hardware supply chains.

Benefits:

Competitive total compensation package (cash + equity)
Health, dental, and vision insurance
Retirement plan
Generous PTO policy

We are committed to pay equity and transparency.

Fluidstack is an Equal Employment Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability and protected veterans’ status, or any other characteristic protected by law. Fluidstack will consider for employment qualified applicants with arrest and conviction records pursuant to applicable law.

You will receive a confirmation email once your application has successfully been accepted. If there is an error with your submission and you did not receive a confirmation email, please email careers@fluidstack.io with your resume/CV, the role you've applied for, and the date you submitted your application-- someone from our recruiting team will be in touch.

44:T1820,About Fluidstack We exist to make humanity more free. For most of human history, you farmed or you starved. Technology gave people more time for the things they wanted to do, instead of things they had to do. Powerful AI will be the biggest lever for human choice we've ever built - but only if models are aligned with what humanity actually wants. There are groups building AI who don't share these goals. Whoever deploys frontier compute infrastructure fastest will decide whether AI expands human freedom or shrinks it. We're singularly focused on delivering 10 to 100s of GWs of compute faster than anyone else, rethinking every layer of the stack. We acquire power, design and build data centers, and operate them - with teams spanning hardware and software. Speed and scale are our key differentiators. Come be a part of building civilization-scale infrastructure for AI. We hire people who care deeply about this problem space. If that is you, please apply! How We Operate Be a barrel. Full autonomy. Own things end to end, take on scope without being asked, no permission required to operate outside your core role. Insane urgency. We drive everything forward as fast as possible. Reason from first principles. Challenge every assumption. Zero analogy thinking, no egos, the best idea wins. Love of the game. The frontier of AI is the most interesting problem of our time. We put in long hours at high intensity to push the frontier forward. Build something that actually matters. If you're going to spend your time, spend it on something that matters to the world. The Decision Team Examples of key problems the team is working on Automate the delivery of gigawatts. Every process that takes AI infrastructure from land to live compute becomes software: schedules, decisions, and todos generated from a live knowledge graph instead of chased by hand. Forward-deploy beside the experts. Product teams sit with quality managers, sourcing leads, and deployment engineers on factory floors and sites, and turn their judgment into systems that reach every unit. Deliver every supercomputer faster than the last. Dozens of concurrent projects feed one graph, so every lesson learned at one site becomes a preventive check at all of them. Role Scope Build the schedule that runs the portfolio: one template schedule per building type, instantiated per project, with every activity tied to the materials it consumes, so need-by dates for switchgear, transformers, and cooling fall out of the critical path instead of a spreadsheet, and a slip in permitting reprices the procurement calendar the same hour. Turn the site into structured data: work packages, contractor field entries, rack placements, cable runs, and QA inspections land as records tied to the design revision they were built against, percent complete is computed from evidence rather than reported on a call, and the customer sees delivered against committed without anyone assembling a deck. Ship change orders as a workflow, not an email chain: scope, cost, and schedule impact drafted from the design delta, routed through legal, finance, and the general contractor with the approval trail attached, and the bill of materials and schedule updated the moment the order is signed. Make the design the source of truth: ingest building information models straight into the bill of materials with a reviewable diff on every revision, generate submittal requirements per spec section, and keep sites, buildings, and data halls as one canonical record that every downstream system reads instead of copies. Ship commissioning as software: test procedures generated per asset from the design revision across all five levels, results landing as evidence against the asset they tested, energization gates that check themselves, and handover that opens on proof instead of a signature. What We're Looking For The below is a starting point. We always make space for exceptional people, so if you don't fit this role exactly, tell us where you would . You've shipped production code in Go, Python, or TypeScript, and you pick up whatever language the problem demands. You've built real features on LLM APIs (OpenAI, Anthropic, or open-weight models), MCP servers, and agentic frameworks. You work daily with AI coding tools like Claude Code and Cursor, and you get agents doing useful work autonomously alongside you. You identify problems, design the solution, and ship it without waiting for direction or approval. You've moved fast under deadline while leaving foundations that other engineers extended after you moved on. You've earned credibility with people who build physical things: construction leads, field engineers, schedulers, or commissioning agents, and driven adoption of your software inside their real deadlines. Your product taste shows in what you've shipped: interfaces field crews call obvious, and workflows that match how the work actually happens. Bonus: Construction tech or project controls (P6, schedule engines, critical path). BIM, IFC, or Revit data models. Commissioning, BMS/EPMS, or controls protocols. Document control and submittal workflows. Graph data models. Computer vision on site imagery. Benefits: Competitive total compensation package (cash + equity) Health, dental, and vision insurance Retirement plan Generous PTO policy We are committed to pay equity and transparency. Fluidstack is an Equal Employment Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability and protected veterans’ status, or any other characteristic protected by law. Fluidstack will consider for employment qualified applicants with arrest and conviction records pursuant to applicable law. You will receive a confirmation email once your application has successfully been accepted. If there is an error with your submission and you did not receive a confirmation email, please email careers@fluidstack.io with your resume/CV, the role you've applied for, and the date you submitted your application-- someone from our recruiting team will be in touch.45:T20e4,

About Fluidstack

We hire people who care deeply about this problem space. If that is you, please apply!

How We Operate

Be a barrel. Full autonomy. Own things end to end, take on scope without being asked, no permission required to operate outside your core role.
Insane urgency. We drive everything forward as fast as possible.
Reason from first principles. Challenge every assumption. Zero analogy thinking, no egos, the best idea wins.
Love of the game. The frontier of AI is the most interesting problem of our time. We put in long hours at high intensity to push the frontier forward.
Build something that actually matters. If you're going to spend your time, spend it on something that matters to the world.

The Decision Team

Examples of key problems the team is working on

Automate the delivery of gigawatts. Every process that takes AI infrastructure from land to live compute becomes software: schedules, decisions, and todos generated from a live knowledge graph instead of chased by hand.
Forward-deploy beside the experts. Product teams sit with quality managers, sourcing leads, and deployment engineers on factory floors and sites, and turn their judgment into systems that reach every unit.
Deliver every supercomputer faster than the last. Dozens of concurrent projects feed one graph, so every lesson learned at one site becomes a preventive check at all of them.

Role Scope

Build the schedule that runs the portfolio: one template schedule per building type, instantiated per project, with every activity tied to the materials it consumes, so need-by dates for switchgear, transformers, and cooling fall out of the critical path instead of a spreadsheet, and a slip in permitting reprices the procurement calendar the same hour.
Turn the site into structured data: work packages, contractor field entries, rack placements, cable runs, and QA inspections land as records tied to the design revision they were built against, percent complete is computed from evidence rather than reported on a call, and the customer sees delivered against committed without anyone assembling a deck.
Ship change orders as a workflow, not an email chain: scope, cost, and schedule impact drafted from the design delta, routed through legal, finance, and the general contractor with the approval trail attached, and the bill of materials and schedule updated the moment the order is signed.
Make the design the source of truth: ingest building information models straight into the bill of materials with a reviewable diff on every revision, generate submittal requirements per spec section, and keep sites, buildings, and data halls as one canonical record that every downstream system reads instead of copies.
Ship commissioning as software: test procedures generated per asset from the design revision across all five levels, results landing as evidence against the asset they tested, energization gates that check themselves, and handover that opens on proof instead of a signature.

What We're Looking For

The below is a starting point. We always make space for exceptional people, so if you don't fit this role exactly, tell us where you would.

You've shipped production code in Go, Python, or TypeScript, and you pick up whatever language the problem demands.
You've built real features on LLM APIs (OpenAI, Anthropic, or open-weight models), MCP servers, and agentic frameworks.
You work daily with AI coding tools like Claude Code and Cursor, and you get agents doing useful work autonomously alongside you.
You identify problems, design the solution, and ship it without waiting for direction or approval.
You've moved fast under deadline while leaving foundations that other engineers extended after you moved on.
You've earned credibility with people who build physical things: construction leads, field engineers, schedulers, or commissioning agents, and driven adoption of your software inside their real deadlines.
Your product taste shows in what you've shipped: interfaces field crews call obvious, and workflows that match how the work actually happens.
Bonus: Construction tech or project controls (P6, schedule engines, critical path). BIM, IFC, or Revit data models. Commissioning, BMS/EPMS, or controls protocols. Document control and submittal workflows. Graph data models. Computer vision on site imagery.

Benefits:

Competitive total compensation package (cash + equity)
Health, dental, and vision insurance
Retirement plan
Generous PTO policy

We are committed to pay equity and transparency.

46:T191a,About Fluidstack We exist to make humanity more free. For most of human history, you farmed or you starved. Technology gave people more time for the things they wanted to do, instead of things they had to do. Powerful AI will be the biggest lever for human choice we've ever built - but only if models are aligned with what humanity actually wants. There are groups building AI who don't share these goals. Whoever deploys frontier compute infrastructure fastest will decide whether AI expands human freedom or shrinks it. We're singularly focused on delivering 10 to 100s of GWs of compute faster than anyone else, rethinking every layer of the stack. We acquire power, design and build data centers, and operate them - with teams spanning hardware and software. Speed and scale are our key differentiators. Come be a part of building civilization-scale infrastructure for AI. We hire people who care deeply about this problem space. If that is you, please apply! How We Operate Be a barrel. Full autonomy. Own things end to end, take on scope without being asked, no permission required to operate outside your core role. Insane urgency. We drive everything forward as fast as possible. Reason from first principles. Challenge every assumption. Zero analogy thinking, no egos, the best idea wins. Love of the game. The frontier of AI is the most interesting problem of our time. We put in long hours at high intensity to push the frontier forward. Build something that actually matters. If you're going to spend your time, spend it on something that matters to the world. The Decision Team Examples of key problems the team is working on Automate the delivery of gigawatts. Every process that takes AI infrastructure from land to live compute becomes software: schedules, decisions, and todos generated from a live knowledge graph instead of chased by hand. Forward-deploy beside the experts. Product teams sit with quality managers, sourcing leads, and deployment engineers on factory floors and sites, and turn their judgment into systems that reach every unit. Deliver every supercomputer faster than the last. Dozens of concurrent projects feed one graph, so every lesson learned at one site becomes a preventive check at all of them. Role Scope Build the hiring machine as software. Hiring is the company's biggest bottleneck. Every role, seat, and interview loop already lives in one graph, and you take it the rest of the way: the recruiting system synced both directions, interviewer supply and load visible to every hiring manager, and onboarding generated the moment an offer is signed, so day one starts with the job instead of the checklist. Build the cash view the company runs on: today no system derives near term spend, so finance forecasts by hand and misses what construction already committed. You turn purchase orders, contract terms, and build milestones into an order schedule, a cash demand curve by month, and a payment schedule that recompute as procurement and construction move. Build one capital pipeline from approved budget to released capital to spent dollar, gated on build milestones the delivery system already verifies, with the proof chain behind every financing drawdown assembled automatically, so releasing nine figures takes a day of judgment and funding follows construction within days. Parse every MSA, change order, and side letter into obligations, prices, deadlines, penalties, and SLAs, so a delivery lead gets what we owe a customer at a site with the clause attached, a change order arrives with its legal and finance checks done, and the corpus of executed deals prices the next negotiation. Embed forward-deployed with finance, treasury, accounting, legal, and people teams through a close, a drawdown, a negotiation, and a hiring loop, and turn their approvals, contract terms, screens, and reconciliations into structured data and generated work, including a continuous close where capitalization and depreciation post as events the moment they happen. What We're Looking For The below is a starting point. We always make space for exceptional people, so if you don't fit this role exactly, tell us where you would . You've shipped production code in Go, Python, or TypeScript, and you pick up whatever language the problem demands. You've built real features on LLM APIs (OpenAI, Anthropic, or open-weight models), MCP servers, and agentic frameworks. You work daily with AI coding tools like Claude Code and Cursor, and you get agents doing useful work autonomously alongside you. You identify problems, design the solution, and ship it without waiting for direction or approval. You've moved fast under deadline while leaving foundations that other engineers extended after you moved on. You've earned credibility with experts outside engineering: finance leads, lawyers, recruiters, or auditors, and driven adoption of your software inside their real deadlines, like a working close calendar or an offer that has to go out today. Your product taste shows in what you've shipped: a controller, a recruiter, or a lawyer calls your interface obvious, and the workflow matches how their work actually happens. Bonus: ERP and fixed asset accounting. FP&A, cash forecasting, and capital planning. Structured finance or treasury operations. Contract lifecycle systems. ATS and HRIS integrations (Ashby, Greenhouse, Rippling, Workday). LLM extraction over legal and financial documents. Benefits: Competitive total compensation package (cash + equity) Health, dental, and vision insurance Retirement plan Generous PTO policy We are committed to pay equity and transparency. Fluidstack is an Equal Employment Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability and protected veterans’ status, or any other characteristic protected by law. Fluidstack will consider for employment qualified applicants with arrest and conviction records pursuant to applicable law. You will receive a confirmation email once your application has successfully been accepted. If there is an error with your submission and you did not receive a confirmation email, please email careers@fluidstack.io with your resume/CV, the role you've applied for, and the date you submitted your application-- someone from our recruiting team will be in touch.47:T2197,

About Fluidstack

We hire people who care deeply about this problem space. If that is you, please apply!

How We Operate

Be a barrel. Full autonomy. Own things end to end, take on scope without being asked, no permission required to operate outside your core role.
Insane urgency. We drive everything forward as fast as possible.
Reason from first principles. Challenge every assumption. Zero analogy thinking, no egos, the best idea wins.
Love of the game. The frontier of AI is the most interesting problem of our time. We put in long hours at high intensity to push the frontier forward.
Build something that actually matters. If you're going to spend your time, spend it on something that matters to the world.

The Decision Team

Examples of key problems the team is working on

Automate the delivery of gigawatts. Every process that takes AI infrastructure from land to live compute becomes software: schedules, decisions, and todos generated from a live knowledge graph instead of chased by hand.
Forward-deploy beside the experts. Product teams sit with quality managers, sourcing leads, and deployment engineers on factory floors and sites, and turn their judgment into systems that reach every unit.
Deliver every supercomputer faster than the last. Dozens of concurrent projects feed one graph, so every lesson learned at one site becomes a preventive check at all of them.

Role Scope

Build the hiring machine as software. Hiring is the company's biggest bottleneck. Every role, seat, and interview loop already lives in one graph, and you take it the rest of the way: the recruiting system synced both directions, interviewer supply and load visible to every hiring manager, and onboarding generated the moment an offer is signed, so day one starts with the job instead of the checklist.
Build the cash view the company runs on: today no system derives near term spend, so finance forecasts by hand and misses what construction already committed. You turn purchase orders, contract terms, and build milestones into an order schedule, a cash demand curve by month, and a payment schedule that recompute as procurement and construction move.
Build one capital pipeline from approved budget to released capital to spent dollar, gated on build milestones the delivery system already verifies, with the proof chain behind every financing drawdown assembled automatically, so releasing nine figures takes a day of judgment and funding follows construction within days.
Parse every MSA, change order, and side letter into obligations, prices, deadlines, penalties, and SLAs, so a delivery lead gets what we owe a customer at a site with the clause attached, a change order arrives with its legal and finance checks done, and the corpus of executed deals prices the next negotiation.
Embed forward-deployed with finance, treasury, accounting, legal, and people teams through a close, a drawdown, a negotiation, and a hiring loop, and turn their approvals, contract terms, screens, and reconciliations into structured data and generated work, including a continuous close where capitalization and depreciation post as events the moment they happen.

What We're Looking For

The below is a starting point. We always make space for exceptional people, so if you don't fit this role exactly, tell us where you would.

You've shipped production code in Go, Python, or TypeScript, and you pick up whatever language the problem demands.
You've built real features on LLM APIs (OpenAI, Anthropic, or open-weight models), MCP servers, and agentic frameworks.
You work daily with AI coding tools like Claude Code and Cursor, and you get agents doing useful work autonomously alongside you.
You identify problems, design the solution, and ship it without waiting for direction or approval.
You've moved fast under deadline while leaving foundations that other engineers extended after you moved on.
You've earned credibility with experts outside engineering: finance leads, lawyers, recruiters, or auditors, and driven adoption of your software inside their real deadlines, like a working close calendar or an offer that has to go out today.
Your product taste shows in what you've shipped: a controller, a recruiter, or a lawyer calls your interface obvious, and the workflow matches how their work actually happens.
Bonus: ERP and fixed asset accounting. FP&A, cash forecasting, and capital planning. Structured finance or treasury operations. Contract lifecycle systems. ATS and HRIS integrations (Ashby, Greenhouse, Rippling, Workday). LLM extraction over legal and financial documents.

Benefits:

Competitive total compensation package (cash + equity)
Health, dental, and vision insurance
Retirement plan
Generous PTO policy

We are committed to pay equity and transparency.

48:T18c0,About Fluidstack We exist to make humanity more free. For most of human history, you farmed or you starved. Technology gave people more time for the things they wanted to do, instead of things they had to do. Powerful AI will be the biggest lever for human choice we've ever built - but only if models are aligned with what humanity actually wants. There are groups building AI who don't share these goals. Whoever deploys frontier compute infrastructure fastest will decide whether AI expands human freedom or shrinks it. We're singularly focused on delivering 10 to 100s of GWs of compute faster than anyone else, rethinking every layer of the stack. We acquire power, design and build data centers, and operate them - with teams spanning hardware and software. Speed and scale are our key differentiators. Come be a part of building civilization-scale infrastructure for AI. We hire people who care deeply about this problem space. If that is you, please apply! How We Operate Be a barrel. Full autonomy. Own things end to end, take on scope without being asked, no permission required to operate outside your core role. Insane urgency. We drive everything forward as fast as possible. Reason from first principles. Challenge every assumption. Zero analogy thinking, no egos, the best idea wins. Love of the game. The frontier of AI is the most interesting problem of our time. We put in long hours at high intensity to push the frontier forward. Build something that actually matters. If you're going to spend your time, spend it on something that matters to the world. The Decision Team Examples of key problems the team is working on Automate the delivery of gigawatts. Every process that takes AI infrastructure from land to live compute becomes software: schedules, decisions, and todos generated from a live knowledge graph instead of chased by hand. Forward-deploy beside the experts. Product teams sit with quality managers, sourcing leads, and deployment engineers on factory floors and sites, and turn their judgment into systems that reach every unit. Deliver every supercomputer faster than the last. Dozens of concurrent projects feed one graph, so every lesson learned at one site becomes a preventive check at all of them. Role Scope Turn the bill of materials into the order book: every template bill carries the price and lead time of each line, every project bill locks against a building, and every purchase order derives from it, so tens of billions of dollars of GPUs, optics, switchgear, and cooling are ordered from one source of truth instead of a spreadsheet. Make procurement tracking trustworthy: the rules that decide when a line is late, what its status means, and which lines sit on the critical path run in code against live vendor emails, documents, and general contractor purchase orders through the extractors you ship, so drift is caught the week it happens and the recovery plan is drafted before the vendor calls. Close the loop from receiving to payment: every delivery matches a purchase order line, every warehouse bin knows its serial numbers, cycle counts are scored against an accuracy gate, and finance three-way matches from the same record, so the question "did we get what we paid for" has one answer instead of three. Ship manufacturing as software before the factory opens: manufacturing bills of materials, engineering change notices, and the drawing tree live in one model, inspection and test plans and first article procedures generate per asset from the design revision, and a failed check opens a nonconformance with the evidence attached and rescores the vendor. Embed with sourcing leads, quality managers, factory operators, and warehouse teams, on their floors and in their vendor calls, and turn their judgment into the systems that decide the order schedule, the cash demand by month, and the payment schedule the company plans against. What We're Looking For The below is a starting point. We always make space for exceptional people, so if you don't fit this role exactly, tell us where you would . You've shipped production code in Go, Python, or TypeScript, and you pick up whatever language the problem demands. You've built real features on LLM APIs (OpenAI, Anthropic, or open-weight models), MCP servers, and agentic frameworks, and you've put document extraction into production against messy real inputs. You work daily with AI coding tools like Claude Code and Cursor, and you get agents doing useful work autonomously alongside you. You identify problems, design the solution, and ship it without waiting for direction or approval. You've moved fast under deadline while leaving data models and foundations that other engineers extended after you moved on. You've earned credibility with operators in factories, warehouses, or procurement, and driven adoption of your software inside their real deadlines, like a live production run or a quarter-end close. Your product taste shows in what you've shipped: interfaces the floor calls obvious, and workflows that match how the work actually happens. Bonus: MES, ERP, WMS, or TMS systems. BOM lineage, engineering change control, and serialized component traceability. Supplier quality (NCRs, CAPA, FAI). Three-way match and receiving reconciliation. LLM extraction over vendor documents. High-volume hardware supply chains. Benefits: Competitive total compensation package (cash + equity) Health, dental, and vision insurance Retirement plan Generous PTO policy We are committed to pay equity and transparency. Fluidstack is an Equal Employment Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability and protected veterans’ status, or any other characteristic protected by law. Fluidstack will consider for employment qualified applicants with arrest and conviction records pursuant to applicable law. You will receive a confirmation email once your application has successfully been accepted. If there is an error with your submission and you did not receive a confirmation email, please email careers@fluidstack.io with your resume/CV, the role you've applied for, and the date you submitted your application-- someone from our recruiting team will be in touch.49:T2139,

About Fluidstack

We hire people who care deeply about this problem space. If that is you, please apply!

How We Operate

Be a barrel. Full autonomy. Own things end to end, take on scope without being asked, no permission required to operate outside your core role.
Insane urgency. We drive everything forward as fast as possible.
Reason from first principles. Challenge every assumption. Zero analogy thinking, no egos, the best idea wins.
Love of the game. The frontier of AI is the most interesting problem of our time. We put in long hours at high intensity to push the frontier forward.
Build something that actually matters. If you're going to spend your time, spend it on something that matters to the world.

The Decision Team

Examples of key problems the team is working on

Automate the delivery of gigawatts. Every process that takes AI infrastructure from land to live compute becomes software: schedules, decisions, and todos generated from a live knowledge graph instead of chased by hand.
Forward-deploy beside the experts. Product teams sit with quality managers, sourcing leads, and deployment engineers on factory floors and sites, and turn their judgment into systems that reach every unit.
Deliver every supercomputer faster than the last. Dozens of concurrent projects feed one graph, so every lesson learned at one site becomes a preventive check at all of them.

Role Scope

Turn the bill of materials into the order book: every template bill carries the price and lead time of each line, every project bill locks against a building, and every purchase order derives from it, so tens of billions of dollars of GPUs, optics, switchgear, and cooling are ordered from one source of truth instead of a spreadsheet.
Make procurement tracking trustworthy: the rules that decide when a line is late, what its status means, and which lines sit on the critical path run in code against live vendor emails, documents, and general contractor purchase orders through the extractors you ship, so drift is caught the week it happens and the recovery plan is drafted before the vendor calls.
Close the loop from receiving to payment: every delivery matches a purchase order line, every warehouse bin knows its serial numbers, cycle counts are scored against an accuracy gate, and finance three-way matches from the same record, so the question "did we get what we paid for" has one answer instead of three.
Ship manufacturing as software before the factory opens: manufacturing bills of materials, engineering change notices, and the drawing tree live in one model, inspection and test plans and first article procedures generate per asset from the design revision, and a failed check opens a nonconformance with the evidence attached and rescores the vendor.
Embed with sourcing leads, quality managers, factory operators, and warehouse teams, on their floors and in their vendor calls, and turn their judgment into the systems that decide the order schedule, the cash demand by month, and the payment schedule the company plans against.

What We're Looking For

The below is a starting point. We always make space for exceptional people, so if you don't fit this role exactly, tell us where you would.

You've shipped production code in Go, Python, or TypeScript, and you pick up whatever language the problem demands.
You've built real features on LLM APIs (OpenAI, Anthropic, or open-weight models), MCP servers, and agentic frameworks, and you've put document extraction into production against messy real inputs.
You work daily with AI coding tools like Claude Code and Cursor, and you get agents doing useful work autonomously alongside you.
You identify problems, design the solution, and ship it without waiting for direction or approval.
You've moved fast under deadline while leaving data models and foundations that other engineers extended after you moved on.
You've earned credibility with operators in factories, warehouses, or procurement, and driven adoption of your software inside their real deadlines, like a live production run or a quarter-end close.
Your product taste shows in what you've shipped: interfaces the floor calls obvious, and workflows that match how the work actually happens.
Bonus: MES, ERP, WMS, or TMS systems. BOM lineage, engineering change control, and serialized component traceability. Supplier quality (NCRs, CAPA, FAI). Three-way match and receiving reconciliation. LLM extraction over vendor documents. High-volume hardware supply chains.

Benefits:

Competitive total compensation package (cash + equity)
Health, dental, and vision insurance
Retirement plan
Generous PTO policy

We are committed to pay equity and transparency.

4a:T2448,About Parloa Parloa’s mission is to make every customer conversation feel effortless for both customers and the companies serving them. As agentic AI accelerates, Parloans are shaping the foundation of a new era in customer experience, one where customer support is no longer transactions, but meaningful exchanges. It is not just a vision; Parloa has powered over ONE BILLION interactions between global enterprise brands and their customers, with companies like Booking.com , HealthEquity, Allianz, SAP, BarmeniaGothaer, and TUI already deploying Parloa at scale. About the role: As a Staff Product Engineer , you will work directly inside our most strategic enterprise customers. You will build the software that makes their AI agents work in production, and be measured by the difference it makes for the customer. Our agents are built by business users in Parloa’s platform, AMP. AMP already comes with a broad library of tools and integrations. Your job is the part AMP does not cover yet for a given customer: the last connection into their systems, or a capability the product does not have. Our customers are large enterprises in financial services, telecom, insurance, travel and retail. For them, a working prototype is where an engagement starts, not where it ends. The work comes in two forms. Some engagements run for years. You stay with the account, deepen its integrations and help it grow into new use cases. Others are short experiments. You test a new idea with a real customer and decide quickly whether it is worth building. At Staff level, you take on the hardest cases of both: accounts where a pilot-grade solution would fail in production, and capabilities nobody at Parloa has built before. Staff means harder problems and more ambiguity, not less hands-on work. On many accounts, you work alongside a Deployment Strategist. Think of the two of you as the account’s leadership team. The Deployment Strategist acts as its CPO and defines what success looks like. You act as its CTO and own the architecture and the build. On accounts without a Deployment Strategist, you own the engineering yourself. Areas of ownership: Lead the hardest engagements. Run complex programmes with several workstreams. Make the architecture decisions that span Parloa’s platform and several parts of the customer’s organization. Build what AMP does not cover yet. Systems of record, telephony and contact-center integrations, state management, data readiness, multi-agent orchestration and retrieval. When the answer is unclear, build a prototype quickly and decide whether it is worth pursuing before Parloa invests a full quarter in it. Own production quality and security. Evals, production readiness, observability and incident playbooks for enterprise-scale traffic. Meet the data handling standards a regulated customer expects, including PII, access control and audit trails. Debug live issues, whether they are technical or caused by model behaviour. Grow the account, and share what belongs in the product. Drive the second use case, the second business unit, the renewal. Set the delivery standard for other engineers on complex accounts. When something you build would help every customer, flag it. That is how Parloa decides what goes into the core product. Mentor other Product Engineers , and take part in interviewing and hiring. Use AI well, and set the standard for it. Decide how AI-assisted work is validated before it reaches a customer’s production traffic. Represent Parloa with senior technical stakeholders. Work directly with the customer’s principal engineers, architects and security leaders. Be the engineer commercial teams bring in when a prospect asks how the solution would work for them. What this role is not: Not running the Parloa platform itself. That is a separate engineering role. The platform is your foundation, and you build what a specific customer needs on top of it. Not agent configuration. Business users design and configure agents in AMP. You build the integrations and capabilities those agents need. Not a people-management role. You are an individual contributor and write and review code every week. OUR TECH STACK: Go, TypeScript, Node.js, Python, FastAPI, frontier LLMs, MCP, Azure, Kubernetes, Docker, Terraform, MongoDB, Redis, Kafka/NATS You will not use all of these, and most engagements add something new. Who you are: 8+ years of professional software development , delivering production systems end to end. Fluent in TypeScript or Python, or able to get there quickly. 5+ years leading the design and architecture of large-scale production systems in customer-facing or enterprise environments, with a focus on reliability, scale and failure modes. Experience leading agentic, spec-driven development in production. You know when a coding agent’s output is wrong and what checks it needs before it ships. A track record in long-term accounts or fast experiments. You have owned a strategic enterprise relationship over several years, through an expansion, a renewal or a leadership change. Or you have tested new ideas at the frontier and decided quickly whether they were worth building. Experience as a mentor or tech lead , helping engineers become independent. Comfortable with ambiguity and with customers. You work well in complex organizations, build trust quickly with new customer teams, spot risks early and keep your judgment when the stakes are high. Willingness to travel up to 25% to customer sites, depending on the engagement. Strong spoken and written communication skills in English. Nice to have: Experience building or deploying an LLM-powered system used by real users in production Hands-on experience with eval frameworks, agent tooling, RAG, prompt engineering or multi-agent orchestration (few candidates will have all of these) A founder or founding-engineer background Forward-deployed, solutions engineering, product engineering or strategic-partner experience at an enterprise software or AI company Voice or conversational AI, or contact center, telephony or CCaaS integration Experience in regulated industries such as banking, insurance or telecom Our engineering values: At Parloa, we hire, promote, and evaluate based on cultural and technical excellence. You’ll thrive here if you: Earn Trust – act with transparency and reliability. Obsess Over Customers – always start with the customer problem. Think Big – set bold goals and pursue them fearlessly. Have Integrity – do the right thing, especially when it’s hard. Disagree & Commit – debate with candor, align fast, and deliver. Own It – take full responsibility for outcomes, not just tasks. Dive Deep – master the details without losing sight of the big picture. Bias for Action – move fast, decide quickly, iterate fearlessly. Deliver Value – measure success by customer and business impact. Insist on the Highest Standards – raise the bar continuously. Go the Extra Mile – make the impossible possible. Hire & Elevate the Best – raise the bar with every hire. Our recruiting process: Recruiter Call → Technical Peer Interview → 360° Tech Interview Loop → Bar Raiser Why Parloa? We’re at the beginning of a new era in customer experience, one where AI doesn’t just respond, but understands, reasons, and takes action. We’re building agentic AI that enterprises trust with their most important customer moments: complex questions, high volumes, real stakes. When millions of people reach out to a brand, those interactions aren’t just support tickets; they’re defining experiences. We’re here to raise the standard: making every conversation seamless, intelligent, and genuinely helpful. If you care about shaping how businesses and customers connect at scale—and want your work to matter in real, everyday moments—this is where you do it. At Parloa, ownership isn’t a buzzword; it means being accountable for outcomes, not just tasks. We operate in a category that’s evolving fast, where the bar is high, and the problems are complex. We hire people who think in solutions, communicate with clarity, and follow through. People who are comfortable making decisions, taking responsibility, and raising the standard for themselves and those around them. We’ve grown beyond the earliest startup phase, but we’ve kept the intensity: fast execution, direct feedback, and a strong expectation to contribute meaningfully from day one. Backed by leading global investors like General Catalyst, EQT Ventures, and Altimeter Capital, we’re scaling with a clear ambition: to become the global category leader in enterprise-grade conversational AI. Parloa is committed to upholding the highest data protection standards for our clients' and employees' data. All our employees are instrumental in ensuring the utmost care, GDPR, and ISO compliance, including ISO 27001, in handling sensitive information. Parloa is an e-verify employer in the USA. Please click here to learn more . * We provide equal opportunities to all qualified applicants regardless race, gender, sexual orientation, age, religion, national origin, disability status, socioeconomic background and other characteristics.4b:T2ad5,

About Parloa

Parloa’s mission is to make every customer conversation feel effortless for both customers and the companies serving them. As agentic AI accelerates, Parloans are shaping the foundation of a new era in customer experience, one where customer support is no longer transactions, but meaningful exchanges. It is not just a vision; Parloa has powered over ONE BILLION interactions between global enterprise brands and their customers, with companies like Booking.com, HealthEquity, Allianz, SAP, BarmeniaGothaer, and TUI already deploying Parloa at scale.

About the role:

As a Staff Product Engineer, you will work directly inside our most strategic enterprise customers. You will build the software that makes their AI agents work in production, and be measured by the difference it makes for the customer.

Our agents are built by business users in Parloa’s platform, AMP. AMP already comes with a broad library of tools and integrations. Your job is the part AMP does not cover yet for a given customer: the last connection into their systems, or a capability the product does not have. Our customers are large enterprises in financial services, telecom, insurance, travel and retail. For them, a working prototype is where an engagement starts, not where it ends.

The work comes in two forms. Some engagements run for years. You stay with the account, deepen its integrations and help it grow into new use cases. Others are short experiments. You test a new idea with a real customer and decide quickly whether it is worth building. At Staff level, you take on the hardest cases of both: accounts where a pilot-grade solution would fail in production, and capabilities nobody at Parloa has built before. Staff means harder problems and more ambiguity, not less hands-on work.

On many accounts, you work alongside a Deployment Strategist. Think of the two of you as the account’s leadership team. The Deployment Strategist acts as its CPO and defines what success looks like. You act as its CTO and own the architecture and the build. On accounts without a Deployment Strategist, you own the engineering yourself.

Areas of ownership:

Lead the hardest engagements. Run complex programmes with several workstreams. Make the architecture decisions that span Parloa’s platform and several parts of the customer’s organization.
Build what AMP does not cover yet. Systems of record, telephony and contact-center integrations, state management, data readiness, multi-agent orchestration and retrieval. When the answer is unclear, build a prototype quickly and decide whether it is worth pursuing before Parloa invests a full quarter in it.
Own production quality and security. Evals, production readiness, observability and incident playbooks for enterprise-scale traffic. Meet the data handling standards a regulated customer expects, including PII, access control and audit trails. Debug live issues, whether they are technical or caused by model behaviour.
Grow the account, and share what belongs in the product. Drive the second use case, the second business unit, the renewal. Set the delivery standard for other engineers on complex accounts. When something you build would help every customer, flag it. That is how Parloa decides what goes into the core product.
Mentor other Product Engineers, and take part in interviewing and hiring.
Use AI well, and set the standard for it. Decide how AI-assisted work is validated before it reaches a customer’s production traffic.
Represent Parloa with senior technical stakeholders. Work directly with the customer’s principal engineers, architects and security leaders. Be the engineer commercial teams bring in when a prospect asks how the solution would work for them.

What this role is not:

Not running the Parloa platform itself. That is a separate engineering role. The platform is your foundation, and you build what a specific customer needs on top of it.
Not agent configuration. Business users design and configure agents in AMP. You build the integrations and capabilities those agents need.
Not a people-management role. You are an individual contributor and write and review code every week.

OUR TECH STACK:

Go, TypeScript, Node.js, Python, FastAPI, frontier LLMs, MCP, Azure, Kubernetes, Docker, Terraform, MongoDB, Redis, Kafka/NATS

You will not use all of these, and most engagements add something new.

Who you are:

8+ years of professional software development, delivering production systems end to end. Fluent in TypeScript or Python, or able to get there quickly.
5+ years leading the design and architecture of large-scale production systems in customer-facing or enterprise environments, with a focus on reliability, scale and failure modes.
Experience leading agentic, spec-driven development in production. You know when a coding agent’s output is wrong and what checks it needs before it ships.
A track record in long-term accounts or fast experiments. You have owned a strategic enterprise relationship over several years, through an expansion, a renewal or a leadership change. Or you have tested new ideas at the frontier and decided quickly whether they were worth building.
Experience as a mentor or tech lead, helping engineers become independent.
Comfortable with ambiguity and with customers. You work well in complex organizations, build trust quickly with new customer teams, spot risks early and keep your judgment when the stakes are high.
Willingness to travel up to 25% to customer sites, depending on the engagement.
Strong spoken and written communication skills in English.

Nice to have:

Experience building or deploying an LLM-powered system used by real users in production
Hands-on experience with eval frameworks, agent tooling, RAG, prompt engineering or multi-agent orchestration (few candidates will have all of these)
A founder or founding-engineer background
Forward-deployed, solutions engineering, product engineering or strategic-partner experience at an enterprise software or AI company
Voice or conversational AI, or contact center, telephony or CCaaS integration
Experience in regulated industries such as banking, insurance or telecom

Our engineering values:

At Parloa, we hire, promote, and evaluate based on cultural and technical excellence. You’ll thrive here if you:

Earn Trust – act with transparency and reliability.
Obsess Over Customers – always start with the customer problem.
Think Big – set bold goals and pursue them fearlessly.
Have Integrity – do the right thing, especially when it’s hard.
Disagree & Commit – debate with candor, align fast, and deliver.
Own It – take full responsibility for outcomes, not just tasks.
Dive Deep – master the details without losing sight of the big picture.
Bias for Action – move fast, decide quickly, iterate fearlessly.
Deliver Value – measure success by customer and business impact.
Insist on the Highest Standards – raise the bar continuously.
Go the Extra Mile – make the impossible possible.
Hire & Elevate the Best – raise the bar with every hire.

Our recruiting process:

Recruiter Call → Technical Peer Interview → 360° Tech Interview Loop → Bar Raiser

Why Parloa?

We’re at the beginning of a new era in customer experience, one where AI doesn’t just respond, but understands, reasons, and takes action. We’re building agentic AI that enterprises trust with their most important customer moments: complex questions, high volumes, real stakes. When millions of people reach out to a brand, those interactions aren’t just support tickets; they’re defining experiences. We’re here to raise the standard: making every conversation seamless, intelligent, and genuinely helpful. If you care about shaping how businesses and customers connect at scale—and want your work to matter in real, everyday moments—this is where you do it.

At Parloa, ownership isn’t a buzzword; it means being accountable for outcomes, not just tasks. We operate in a category that’s evolving fast, where the bar is high, and the problems are complex. We hire people who think in solutions, communicate with clarity, and follow through. People who are comfortable making decisions, taking responsibility, and raising the standard for themselves and those around them.

We’ve grown beyond the earliest startup phase, but we’ve kept the intensity: fast execution, direct feedback, and a strong expectation to contribute meaningfully from day one.

Backed by leading global investors like General Catalyst, EQT Ventures, and Altimeter Capital, we’re scaling with a clear ambition: to become the global category leader in enterprise-grade conversational AI.

Parloa is committed to upholding the highest data protection standards for our clients' and employees' data. All our employees are instrumental in ensuring the utmost care, GDPR, and ISO compliance, including ISO 27001, in handling sensitive information.

Parloa is an e-verify employer in the USA. Please click here to learn more.

* We provide equal opportunities to all qualified applicants regardless race, gender, sexual orientation, age, religion, national origin, disability status, socioeconomic background and other characteristics.

4c:T1142,Backend Engineer, AI Integrations 🏔️ About Sierra Studio Sierra Studio is a product development company with a focus on AI – we turn our customers’ ideas into reality! We’re based in Los Angeles, but our teammates live all around the world. We create a wide variety of products for our customers: from LLM-powered web/mobile apps to cutting-edge work at the frontier of AI. 💼 About The Role As a Backend Engineer focused on AI Integrations, you'll lead engineering on AI products that ship production ready generative AI features. You'll architect the stack, write clean code, integrate LLM APIs, own features end-to-end, and work with your teammates to take ideas from prototype to something clients can rely on. This role sits between our Fullstack and AI tracks: strong fullstack engineering skills, plus hands-on experience at the application layer of AI: model APIs, retrieval, agentic engineering. Here's a taste of what you could do at Sierra! Client projects: Lead AI-powered product work for a startup: designing and shipping customer-facing AI experiences, from agent orchestration and tool use to the interfaces and backend systems that support them Internal projects: Improve our own AI tooling and delivery practices Hiring: Help us grow the team and interview future candidates Leadership: Mentor other engineers to level up our team 🔎 Who We’re Looking For We’re looking for a backend engineer with experience building LLM-powered products. If you're excited about this role but don't check every box, we'd love to see your application anyway. Requirements: 5+ years of experience in software engineering, including leading projects or people Backend experience: TypeScript or Python Experience with cloud infrastructure (e.g. Azure, GCP, AWS) Experience with databases (e.g. PostgreSQL, MongoDB, etc) Hands-on experience building with modern LLM APIs (e.g. OpenAI, Anthropic) Experience with agentic products and prompt engineering Experience with the retrieval layer (e.g. RAG, embeddings, vector search) Experience writing and evaluating Claude Skills Know how to choose the right LLM for the right task, balancing quality, cost, and other factors Experience creating and integrating with MCP servers and converting API endpoints into tools Growth mindset: able to quickly learn new languages, frameworks, and more Strong written + verbal communication skills in English Ability to accurately estimate timelines and scope Experience using AI for coding (e.g. Claude Code, Codex) Great to have: Experience with system administration, internal workspaces, and IT infra (e.g. Microsoft 365, Google Workspace, Sharepoint) Experience connecting Cowork / ChatGPT Work to internal data sources Experience with integrating Microsoft 365 with AI systems Experience building data ingestion pipelines Experience in the financial sector Bonus – nice to have: Consulting, product studio, or multi-client delivery experience Things you've built that you'd like to show us — work projects, open source, or side projects all count! 🌎 How We Work Fully remote, with flexible hours. We care about output, not hours online. We support work-life balance for teammates at every life stage. 💖 Our culture Growth mindset : We look to magnify our strengths, take chances, and always learn from our mistakes. Supportive : We keep our egos out of the workplace and support each other when trying new things. Product & customer centric : We care deeply about our customers and clients and always keep their needs top of mind. We build tech to solve real problems. Ownership : We take initiative, even when it is “not our job.” As a startup, we all wear lots of hats. 💡 Why join us? At Sierra Studio, we believe in nurturing talent and creating good vibes at work. We seek inclusion and embrace our differences. We’re all about “growth mindset”, and we look to magnify our strengths, take chances, and always learn from our mistakes. We don’t fit our teammates into boxes, and instead we understand that each person has their own unique set of skills that they bring to the table. If you join us, we’ll embrace who you are today, and give you the space and tools to keep growing. Salary Range: $60k - $100k annually. Your exact compensation will be determined based on a variety of factors that reflect your individual qualifications and how they align with the needs of the role.4d:T1aee,

Backend Engineer, AI Integrations

🏔️ About Sierra Studio

Sierra Studio is a product development company with a focus on AI – we turn our customers’ ideas into reality! We’re based in Los Angeles, but our teammates live all around the world. We create a wide variety of products for our customers: from LLM-powered web/mobile apps to cutting-edge work at the frontier of AI.

💼 About The Role

As a Backend Engineer focused on AI Integrations, you'll lead engineering on AI products that ship production ready generative AI features. You'll architect the stack, write clean code, integrate LLM APIs, own features end-to-end, and work with your teammates to take ideas from prototype to something clients can rely on.

This role sits between our Fullstack and AI tracks: strong fullstack engineering skills, plus hands-on experience at the application layer of AI: model APIs, retrieval, agentic engineering.

Here's a taste of what you could do at Sierra!

Client projects: Lead AI-powered product work for a startup: designing and shipping customer-facing AI experiences, from agent orchestration and tool use to the interfaces and backend systems that support them
Internal projects: Improve our own AI tooling and delivery practices
Hiring: Help us grow the team and interview future candidates
Leadership: Mentor other engineers to level up our team

🔎 Who We’re Looking For

We’re looking for a backend engineer with experience building LLM-powered products.

If you're excited about this role but don't check every box, we'd love to see your application anyway.

Requirements:

5+ years of experience in software engineering, including leading projects or people
Backend experience: TypeScript or Python
Experience with cloud infrastructure (e.g. Azure, GCP, AWS)
Experience with databases (e.g. PostgreSQL, MongoDB, etc)
Hands-on experience building with modern LLM APIs (e.g. OpenAI, Anthropic)
Experience with agentic products and prompt engineering
Experience with the retrieval layer (e.g. RAG, embeddings, vector search)
Experience writing and evaluating Claude Skills
Know how to choose the right LLM for the right task, balancing quality, cost, and other factors
Experience creating and integrating with MCP servers and converting API endpoints into tools
Growth mindset: able to quickly learn new languages, frameworks, and more
Strong written + verbal communication skills in English
Ability to accurately estimate timelines and scope
Experience using AI for coding (e.g. Claude Code, Codex)

Great to have:

Experience with system administration, internal workspaces, and IT infra (e.g. Microsoft 365, Google Workspace, Sharepoint)
Experience connecting Cowork / ChatGPT Work to internal data sources
Experience with integrating Microsoft 365 with AI systems
Experience building data ingestion pipelines
Experience in the financial sector

Bonus – nice to have:

Consulting, product studio, or multi-client delivery experience
Things you've built that you'd like to show us — work projects, open source, or side projects all count!

🌎 How We Work

Fully remote, with flexible hours. We care about output, not hours online. We support work-life balance for teammates at every life stage.

💖 Our culture

Growth mindset: We look to magnify our strengths, take chances, and always learn from our mistakes.
Supportive: We keep our egos out of the workplace and support each other when trying new things.
Product & customer centric: We care deeply about our customers and clients and always keep their needs top of mind. We build tech to solve real problems.
Ownership: We take initiative, even when it is “not our job.” As a startup, we all wear lots of hats.

💡 Why join us?

At Sierra Studio, we believe in nurturing talent and creating good vibes at work. We seek inclusion and embrace our differences. We’re all about “growth mindset”, and we look to magnify our strengths, take chances, and always learn from our mistakes. We don’t fit our teammates into boxes, and instead we understand that each person has their own unique set of skills that they bring to the table. If you join us, we’ll embrace who you are today, and give you the space and tools to keep growing.

Salary Range: $60k - $100k annually.

Your exact compensation will be determined based on a variety of factors that reflect your individual qualifications and how they align with the needs of the role.

4e:T27fc,Senior Platform Engineer AI Engineering | Forward Deployed Engineering Role Description Defense Unicorns is seeking a senior AI Agentic Engineer to embed with a government team and its technology partners building an emerging AI-powered engineering ecosystem. This role will focus on designing, integrating, and operationalizing AI agents and agentic workflows within a secure enterprise environment. The engineer will work across large language models, agent frameworks, enterprise data, APIs, developer tooling, and platform services to turn AI capabilities into reliable, mission-relevant workflows. The ideal candidate is a hands-on engineer who understands both the capabilities and limitations of modern AI systems and can rapidly translate emerging technologies into working solutions. They should be comfortable experimenting with new models and agent frameworks, integrating agents with enterprise systems and tools, evaluating performance and reliability, and working alongside platform engineers and commercial technology partners. This is not a research-only role. The focus is on moving from AI concepts and prototypes to secure, useful, repeatable agentic capabilities that can operate in production environments. Responsibilities Design, build, and integrate AI agents and multi-agent workflows using modern LLM and agentic technologies. Develop agent capabilities that interact with enterprise applications, APIs, data sources, developer tools, and mission systems. Integrate models, agents, tools, and enterprise data into secure workflows that can be deployed in controlled and classified environments. Work with LLMs, RAG, embeddings, vector databases, tool/function calling, structured outputs, MCP, and related technologies. Develop mechanisms for agents to securely discover, access, reason over, and act upon approved enterprise data and services. Prototype and rapidly evaluate emerging models, agent frameworks, and AI capabilities to determine where they can create measurable mission or engineering value. Develop evaluation and testing frameworks for agent accuracy, reliability, safety, latency, cost, and task completion. Implement guardrails and policy controls governing agent behavior, tool access, data access, and human approval points. Integrate agentic capabilities with CI/CD and DevSecOps workflows to support repeatable development, testing, deployment, and lifecycle management. Collaborate with platform engineers to operationalize AI workloads within Kubernetes/OpenShift environments. Work with commercial AI and technology partners to integrate their capabilities into the broader engineering ecosystem. Troubleshoot issues across models, agent frameworks, APIs, data sources, networking, identity, and infrastructure. Develop reusable patterns for agent deployment, configuration, observability, evaluation, and lifecycle management. Document architectures, integration patterns, agent specifications, evaluation results, and operational procedures. Identify recurring AI engineering challenges that can be standardized, automated, or productized into reusable capabilities. Stay current with rapidly evolving agentic AI technologies and assess their applicability to secure government environments. Minimum Experience and Qualifications Active TS/SCI clearance. Hands-on experience building applications or systems using large language models and generative AI. Experience designing and implementing AI agents or agentic workflows. Strong programming experience in Python and/or another modern programming language. Experience integrating AI systems with APIs, enterprise applications, databases, and external tools. Experience with one or more agent frameworks or orchestration approaches. Working knowledge of RAG, embeddings, vector databases, tool/function calling, structured outputs, and prompt engineering. Experience developing software in a Git-based, automated CI/CD environment. Ability to rapidly prototype, test, troubleshoot, and iterate in ambiguous environments. Strong understanding of software engineering fundamentals, including testing, version control, APIs, debugging, and system design. Ability to work effectively alongside platform engineers, government personnel, and multiple technology vendors. Preferred Experience and Qualifications Experience with MCP (Model Context Protocol) and tool-based agent architectures. Experience with multi-agent systems and agent orchestration. Experience with OpenAI, Anthropic, Google, NVIDIA, or comparable foundation model ecosystems. Experience with NVIDIA NIM, NeMo, or similar AI inference/model-serving technologies. Experience with Red Hat OpenShift AI or Kubernetes-based AI platforms. Experience with agent evaluation and observability frameworks. Experience implementing AI guardrails, policy enforcement, human-in-the-loop workflows, or AI safety controls. Experience with LangGraph, LangChain, LlamaIndex, Semantic Kernel, or comparable frameworks. Experience with RAG pipelines, vector databases, knowledge graphs, and enterprise search. Experience deploying AI systems in Secret or TS/SCI environments. Experience with disconnected or air-gapped AI environments. Experience integrating AI into developer/DevSecOps workflows. Familiarity with GitLab, Jira, Confluence, Xacta, or similar enterprise engineering and compliance tools. Experience working with GPUs, model serving, inference optimization, or AI infrastructure. Familiarity with UDS, Zarf, Pepr, Iron Bank, or similar secure software delivery technologies. Experience taking AI prototypes into repeatable, production-ready capabilities. Ability to evaluate new AI technologies quickly and make pragmatic build/buy/integrate decisions. What Success Looks Like Agentic capabilities move from prototype to mission use quickly, with working agents integrated into real government workflows rather than remaining isolated demos. Agents reliably connect to approved enterprise data, tools, and services, with appropriate identity, permissions, guardrails, and human-in-the-loop controls. AI capabilities are measurable and trustworthy, with repeatable evaluation methods for accuracy, reliability, task completion, security, and operational performance. Reusable agent patterns emerge, reducing the engineering required to build and deploy the next agent or AI-enabled workflow. AI integrates cleanly with the broader platform architecture, working effectively across the data, DevSecOps, Kubernetes/OpenShift, model, and application layers rather than creating another technology silo. The engineer becomes a trusted AI technical advisor to the Mission Hero, proactively identifying where agentic technology can create mission value—and equally important, where AI is not the right solution. Full compensation packages are based on candidate experience. Compensation ranges are established using national benchmarking data and apply across all geographic locations within the United States. Remote - USA $148,750 — $201,250 USD Who We Are Defense Unicorns delivers mission value by streamlining software delivery so our customers can focus on the most important challenges. We share a vision of freedom and security for the advancement of progress and innovation. Our commitment to this vision, and to our mission-driven customers, means a commitment to speed, user experience and optionality, without compromising security. Our team is composed of innovators, software engineers, and veterans with decades of experience delivering technology programs across the federal market. What We Do We create and deliver secure solutions for continuous software integration and delivery. Defense Unicorns consolidates the best practices for security pipelines, testing, and deployment automation in order to meet the high security requirements valued by mission owners. Our solutions are agnostic by design and we believe that growing a robust ecosystem of secure, cloud-native software solutions can help enterprise customers inside and outside the federal market buy and integrate software more easily. Who We Serve Defense Unicorns’ customers are mission-focused leaders across public and private enterprises. We proudly support defense and civil agencies across the U.S. government and we work closely with the creators of leading-edge software solutions to deliver value to the mission-owner by improving the security and consumability of commercial software products. What We Work On Kubernetes Cloud Environments (AWS/GCP and Azure) Infrastructure-as-code (like Terraform/Pulumi) Continuous Delivery and automation tooling GitOps Containers CNCF projects and open source products and packages Helm/Kustomize-Value Stream Mapping Building and improving security delivery Building Kubernetes and cloud native applications Benefits Our Unicorns Enjoy Health: Medical/Dental/Vision Premiums are 100% Company Paid Health Savings Account Life Insurance Disability Insurance Financial: 401k Retirement Plan Company Stock Options Home Office Budget Leave: We offer all full-time Unicorns Flexible Time Off (FTO) plus all Federal Holidays, one week for Thanksgiving, and two weeks for Christmas and New Year’s Paid Parental Leave Learning: Reimbursement for approved trainings/subscriptions Conferences (travel, lodging, and fees) Don’t have all the preferred experience or qualifications? Studies show that underrepresented groups like women and people of color are less likely to apply to jobs if they don't meet every requirement listed. At Defense Unicorns, we're committed to diversity. If you're enthusiastic about the role but don't match every criteria, we encourage you to apply. You could be the perfect fit for this or another role! Defense Unicorns is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, disability, sex, sexual orientation, gender identity or expression, age, national origin, veteran status, genetic information, union status and/or beliefs, or any other characteristic protected by federal, state, or local law. CCPA DISCLOSURE4f:T2d7d,

Senior Platform Engineer

AI Engineering | Forward Deployed Engineering

Role Description

Defense Unicorns is seeking a senior AI Agentic Engineer to embed with a government team and its technology partners building an emerging AI-powered engineering ecosystem.

This role will focus on designing, integrating, and operationalizing AI agents and agentic workflows within a secure enterprise environment. The engineer will work across large language models, agent frameworks, enterprise data, APIs, developer tooling, and platform services to turn AI capabilities into reliable, mission-relevant workflows.

The ideal candidate is a hands-on engineer who understands both the capabilities and limitations of modern AI systems and can rapidly translate emerging technologies into working solutions. They should be comfortable experimenting with new models and agent frameworks, integrating agents with enterprise systems and tools, evaluating performance and reliability, and working alongside platform engineers and commercial technology partners.

This is not a research-only role. The focus is on moving from AI concepts and prototypes to secure, useful, repeatable agentic capabilities that can operate in production environments.

Responsibilities

Design, build, and integrate AI agents and multi-agent workflows using modern LLM and agentic technologies.
Develop agent capabilities that interact with enterprise applications, APIs, data sources, developer tools, and mission systems.
Integrate models, agents, tools, and enterprise data into secure workflows that can be deployed in controlled and classified environments.
Work with LLMs, RAG, embeddings, vector databases, tool/function calling, structured outputs, MCP, and related technologies.
Develop mechanisms for agents to securely discover, access, reason over, and act upon approved enterprise data and services.
Prototype and rapidly evaluate emerging models, agent frameworks, and AI capabilities to determine where they can create measurable mission or engineering value.
Develop evaluation and testing frameworks for agent accuracy, reliability, safety, latency, cost, and task completion.
Implement guardrails and policy controls governing agent behavior, tool access, data access, and human approval points.
Integrate agentic capabilities with CI/CD and DevSecOps workflows to support repeatable development, testing, deployment, and lifecycle management.
Collaborate with platform engineers to operationalize AI workloads within Kubernetes/OpenShift environments.
Work with commercial AI and technology partners to integrate their capabilities into the broader engineering ecosystem.
Troubleshoot issues across models, agent frameworks, APIs, data sources, networking, identity, and infrastructure.
Develop reusable patterns for agent deployment, configuration, observability, evaluation, and lifecycle management.
Document architectures, integration patterns, agent specifications, evaluation results, and operational procedures.
Identify recurring AI engineering challenges that can be standardized, automated, or productized into reusable capabilities.
Stay current with rapidly evolving agentic AI technologies and assess their applicability to secure government environments.

Minimum Experience and Qualifications

Active TS/SCI clearance.
Hands-on experience building applications or systems using large language models and generative AI.
Experience designing and implementing AI agents or agentic workflows.
Strong programming experience in Python and/or another modern programming language.
Experience integrating AI systems with APIs, enterprise applications, databases, and external tools.
Experience with one or more agent frameworks or orchestration approaches.
Working knowledge of RAG, embeddings, vector databases, tool/function calling, structured outputs, and prompt engineering.
Experience developing software in a Git-based, automated CI/CD environment.
Ability to rapidly prototype, test, troubleshoot, and iterate in ambiguous environments.
Strong understanding of software engineering fundamentals, including testing, version control, APIs, debugging, and system design.
Ability to work effectively alongside platform engineers, government personnel, and multiple technology vendors.

Preferred Experience and Qualifications

Experience with MCP (Model Context Protocol) and tool-based agent architectures.
Experience with multi-agent systems and agent orchestration.
Experience with OpenAI, Anthropic, Google, NVIDIA, or comparable foundation model ecosystems.
Experience with NVIDIA NIM, NeMo, or similar AI inference/model-serving technologies.
Experience with Red Hat OpenShift AI or Kubernetes-based AI platforms.
Experience with agent evaluation and observability frameworks.
Experience implementing AI guardrails, policy enforcement, human-in-the-loop workflows, or AI safety controls.
Experience with LangGraph, LangChain, LlamaIndex, Semantic Kernel, or comparable frameworks.
Experience with RAG pipelines, vector databases, knowledge graphs, and enterprise search.
Experience deploying AI systems in Secret or TS/SCI environments.
Experience with disconnected or air-gapped AI environments.
Experience integrating AI into developer/DevSecOps workflows.
Familiarity with GitLab, Jira, Confluence, Xacta, or similar enterprise engineering and compliance tools.
Experience working with GPUs, model serving, inference optimization, or AI infrastructure.
Familiarity with UDS, Zarf, Pepr, Iron Bank, or similar secure software delivery technologies.
Experience taking AI prototypes into repeatable, production-ready capabilities.
Ability to evaluate new AI technologies quickly and make pragmatic build/buy/integrate decisions.

What Success Looks Like

Agentic capabilities move from prototype to mission use quickly, with working agents integrated into real government workflows rather than remaining isolated demos.
Agents reliably connect to approved enterprise data, tools, and services, with appropriate identity, permissions, guardrails, and human-in-the-loop controls.
AI capabilities are measurable and trustworthy, with repeatable evaluation methods for accuracy, reliability, task completion, security, and operational performance.
Reusable agent patterns emerge, reducing the engineering required to build and deploy the next agent or AI-enabled workflow.
AI integrates cleanly with the broader platform architecture, working effectively across the data, DevSecOps, Kubernetes/OpenShift, model, and application layers rather than creating another technology silo.
The engineer becomes a trusted AI technical advisor to the Mission Hero, proactively identifying where agentic technology can create mission value—and equally important, where AI is not the right solution.

Full compensation packages are based on candidate experience. Compensation ranges are established using national benchmarking data and apply across all geographic locations within the United States.

Remote - USA

$148,750—$201,250 USD

Who We Are

Defense Unicorns delivers mission value by streamlining software delivery so our customers can focus on the most important challenges. We share a vision of freedom and security for the advancement of progress and innovation. Our commitment to this vision, and to our mission-driven customers, means a commitment to speed, user experience and optionality, without compromising security. Our team is composed of innovators, software engineers, and veterans with decades of experience delivering technology programs across the federal market.

What We Do

We create and deliver secure solutions for continuous software integration and delivery. Defense Unicorns consolidates the best practices for security pipelines, testing, and deployment automation in order to meet the high security requirements valued by mission owners. Our solutions are agnostic by design and we believe that growing a robust ecosystem of secure, cloud-native software solutions can help enterprise customers inside and outside the federal market buy and integrate software more easily.

Who We Serve

Defense Unicorns’ customers are mission-focused leaders across public and private enterprises. We proudly support defense and civil agencies across the U.S. government and we work closely with the creators of leading-edge software solutions to deliver value to the mission-owner by improving the security and consumability of commercial software products.

What We Work On

Kubernetes
Cloud Environments (AWS/GCP and Azure)
Infrastructure-as-code (like Terraform/Pulumi)
Continuous Delivery and automation tooling
GitOps
Containers
CNCF projects and open source products and packages
Helm/Kustomize-Value Stream Mapping
Building and improving security delivery
Building Kubernetes and cloud native applications

Benefits Our Unicorns Enjoy

Health:

Medical/Dental/Vision
Premiums are 100% Company Paid
Health Savings Account
Life Insurance
Disability Insurance

Financial:

401k Retirement Plan
Company Stock Options
Home Office Budget

Leave:

We offer all full-time Unicorns Flexible Time Off (FTO) plus all Federal Holidays, one week for Thanksgiving, and two weeks for Christmas and New Year’s
Paid Parental Leave

Learning:

Reimbursement for approved trainings/subscriptions
Conferences (travel, lodging, and fees)

Don’t have all the preferred experience or qualifications? Studies show that underrepresented groups like women and people of color are less likely to apply to jobs if they don't meet every requirement listed.

At Defense Unicorns, we're committed to diversity. If you're enthusiastic about the role but don't match every criteria, we encourage you to apply. You could be the perfect fit for this or another role! Defense Unicorns is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, disability, sex, sexual orientation, gender identity or expression, age, national origin, veteran status, genetic information, union status and/or beliefs, or any other characteristic protected by federal, state, or local law.

CCPA DISCLOSURE

50:T182b,About EarnIn As one of the first pioneers of earned wage access, our passion at EarnIn is building products that deliver real-time financial flexibility for those with the unique needs of living paycheck to paycheck. Our community members access their earnings as they earn them, with options to spend, save, and grow their money without mandatory fees, interest rates, or credit checks. We’re fortunate to have an incredibly experienced leadership team, combined with world-class funding partners like A16Z, Matrix Partners, DST, Ribbit Capital, and a very healthy core business with a tremendous runway. We’re growing fast and are excited to continue bringing world-class talent onboard to help shape the next chapter of our growth journey. POSITION SUMMARY EarnIn is making AI-native engineering a core capability — not an initiative, but how we design, build, and ship. We're not hiring a software engineer who dabbles in AI. We're hiring an AI builder who also writes great software — someone who looks at every step of how we design, build, test, and ship, and asks: why isn't an agent doing this? This is not a side project. The agents and harnesses you build here will run in production, making real decisions for real people — people who depend on EarnIn to access their pay when it matters most. If that bar excites you rather than intimidates you, keep reading. You'll work at the intersection of platform engineering, developer experience, and applied AI — partnering with architects, domain leads,, and product engineers to build the tools, patterns, and guardrails that make AI adoption fast, safe, and durable. The Mountain View base salary range for this full-time position is $228,000 - $279,000, plus equity and benefits. Our salary ranges are determined by role, level, and location. This is a hybrid position in Mountain View that requires in-office work 2 days a week. WHAT YOU'LL DO Agents that take real actions. You'll design how agents think — prompts, reasoning chains, tool calls, and the full architecture beneath them. From MCP servers and agent scaffolding to context harnesses and a Skills Marketplace, you're building the layer every squad at EarnIn builds on. Not a proof of concept. Production infrastructure. A PDLC that doesn't look like 2022. The product development lifecycle is overdue for a rethink. You'll challenge each stage — from scoping and design through to review, testing, deployment, and monitoring — and replace manual friction with agentic workflows wherever it makes sense. Evaluation pipelines, automated PR hygiene, deployment gating, generation-to-merge metrics: you'll build the scaffolding that makes governed AI fast, not slow. Evals that actually mean something. You'll own the evaluation infrastructure — building the pipelines, benchmarks, and quality gates that tell us whether our AI is working, degrading, or ready to ship. That means designing eval harnesses for AI-assisted workflows, setting generation-to-merge and review latency baselines, and making model quality visible and trustworthy across teams. If you've built evals that caught real problems before they hit production, you'll fit right in. Pilots that graduate to production. EarnIn's engineering leads are running experiments. You'll be the person who takes what's working and turns it into something the next team can fork and ship in a week. Less "interesting prototype," more "thing we rely on." You’ll build reusable libraries, templates, and reference implementations that give squads a running start on AI integration, so no one has to solve the same problem twice WHAT SUCCESS LOOKS LIKE Teams across engineering can ship AI-assisted features faster, with fewer rework loops The harnesses you build are actively used and well-documented AI pilot quality and safety metrics are visible, trustworthy, and improving WHAT WE'RE LOOKING FOR 4+ years of full-time software engineering experience, with at least 2 years building tooling, platforms, or internal developer products Bachelor’s, Master’s, or PhD in Computer Science, Computer Engineering, or a related technical discipline, or equivalent industry experience. We care about what you’ve built, not where you studied. Hands-on experience with LLM integration patterns — prompt engineering, RAG pipelines, tool/function calling, and agent architectures Proficiency and comfort working across the stack when needed Experience with MCP, LangChain, or comparable orchestration frameworks Experience with open source LLM models Strong opinions about developer experience and a track record of building things other engineers actually use YOU'LL STAND OUT IF YOU HAVE Hands-on experience with reinforcement learning — especially RLHF, RLAIF, or reward modeling in applied product contexts Experience in fintech or regulated/security-sensitive environments Hands-on work with AI governance — bias evaluation, audit logging, model cards Exposure to multi-step reasoning pipelines or human-in-the-loop system design #LI-Hybrid At EarnIn, we believe that the best way to build a financial system that works for everyday people is by hiring a team that represents our diverse community. Our team is diverse not only in background and experience but also in perspective. We celebrate our diversity and strive to create a culture of belonging. EarnIn does not unlawfully discriminate based on race, color, religion, sex (including pregnancy, childbirth, breastfeeding, or related medical conditions), gender identity, gender expression, national origin, ancestry, citizenship, age, physical or mental disability, legally protected medical condition, family care status, military or veteran status, marital status, registered domestic partner status, sexual orientation, genetic information, or any other basis protected by local, state, or federal laws. EarnIn is an E-Verify participant. EarnIn does not accept unsolicited resumes from individual recruiters or third-party recruiting agencies in response to job postings. No fee will be paid to third parties who submit unsolicited candidates directly to our hiring managers or HR team.51:T1a49,

About EarnIn

As one of the first pioneers of earned wage access, our passion at EarnIn is building products that deliver real-time financial flexibility for those with the unique needs of living paycheck to paycheck. Our community members access their earnings as they earn them, with options to spend, save, and grow their money without mandatory fees, interest rates, or credit checks.

We’re fortunate to have an incredibly experienced leadership team, combined with world-class funding partners like A16Z, Matrix Partners, DST, Ribbit Capital, and a very healthy core business with a tremendous runway. We’re growing fast and are excited to continue bringing world-class talent onboard to help shape the next chapter of our growth journey.

POSITION SUMMARY

EarnIn is making AI-native engineering a core capability — not an initiative, but how we design, build, and ship. We're not hiring a software engineer who dabbles in AI. We're hiring an AI builder who also writes great software — someone who looks at every step of how we design, build, test, and ship, and asks: why isn't an agent doing this? This is not a side project. The agents and harnesses you build here will run in production, making real decisions for real people — people who depend on EarnIn to access their pay when it matters most. If that bar excites you rather than intimidates you, keep reading.

You'll work at the intersection of platform engineering, developer experience, and applied AI — partnering with architects, domain leads,, and product engineers to build the tools, patterns, and guardrails that make AI adoption fast, safe, and durable. The Mountain View base salary range for this full-time position is $228,000 - $279,000, plus equity and benefits. Our salary ranges are determined by role, level, and location. This is a hybrid position in Mountain View that requires in-office work 2 days a week.

WHAT YOU'LL DO

Agents that take real actions. You'll design how agents think — prompts, reasoning chains, tool calls, and the full architecture beneath them. From MCP servers and agent scaffolding to context harnesses and a Skills Marketplace, you're building the layer every squad at EarnIn builds on. Not a proof of concept. Production infrastructure.

A PDLC that doesn't look like 2022. The product development lifecycle is overdue for a rethink. You'll challenge each stage — from scoping and design through to review, testing, deployment, and monitoring — and replace manual friction with agentic workflows wherever it makes sense. Evaluation pipelines, automated PR hygiene, deployment gating, generation-to-merge metrics: you'll build the scaffolding that makes governed AI fast, not slow.

Evals that actually mean something. You'll own the evaluation infrastructure — building the pipelines, benchmarks, and quality gates that tell us whether our AI is working, degrading, or ready to ship. That means designing eval harnesses for AI-assisted workflows, setting generation-to-merge and review latency baselines, and making model quality visible and trustworthy across teams. If you've built evals that caught real problems before they hit production, you'll fit right in.

Pilots that graduate to production. EarnIn's engineering leads are running experiments. You'll be the person who takes what's working and turns it into something the next team can fork and ship in a week. Less "interesting prototype," more "thing we rely on." You’ll build reusable libraries, templates, and reference implementations that give squads a running start on AI integration, so no one has to solve the same problem twice

WHAT SUCCESS LOOKS LIKE

Teams across engineering can ship AI-assisted features faster, with fewer rework loops
The harnesses you build are actively used and well-documented
AI pilot quality and safety metrics are visible, trustworthy, and improving

WHAT WE'RE LOOKING FOR

4+ years of full-time software engineering experience, with at least 2 years building tooling, platforms, or internal developer products
Bachelor’s, Master’s, or PhD in Computer Science, Computer Engineering, or a related technical discipline, or equivalent industry experience. We care about what you’ve built, not where you studied.
Hands-on experience with LLM integration patterns — prompt engineering, RAG pipelines, tool/function calling, and agent architectures
Proficiency and comfort working across the stack when needed
Experience with MCP, LangChain, or comparable orchestration frameworks
Experience with open source LLM models
Strong opinions about developer experience and a track record of building things other engineers actually use

YOU'LL STAND OUT IF YOU HAVE

Hands-on experience with reinforcement learning — especially RLHF, RLAIF, or reward modeling in applied product contexts
Experience in fintech or regulated/security-sensitive environments
Hands-on work with AI governance — bias evaluation, audit logging, model cards
Exposure to multi-step reasoning pipelines or human-in-the-loop system design

#LI-Hybrid

At EarnIn, we believe that the best way to build a financial system that works for everyday people is by hiring a team that represents our diverse community. Our team is diverse not only in background and experience but also in perspective. We celebrate our diversity and strive to create a culture of belonging. EarnIn does not unlawfully discriminate based on race, color, religion, sex (including pregnancy, childbirth, breastfeeding, or related medical conditions), gender identity, gender expression, national origin, ancestry, citizenship, age, physical or mental disability, legally protected medical condition, family care status, military or veteran status, marital status, registered domestic partner status, sexual orientation, genetic information, or any other basis protected by local, state, or federal laws. EarnIn is an E-Verify participant.

EarnIn does not accept unsolicited resumes from individual recruiters or third-party recruiting agencies in response to job postings. No fee will be paid to third parties who submit unsolicited candidates directly to our hiring managers or HR team.

52:T1221,Binance is a leading global blockchain ecosystem behind the world’s largest cryptocurrency exchange by trading volume and registered users. We are trusted by 300+ million people in 100+ countries for our industry-leading security, user fund transparency, trading engine speed, deep liquidity, and an unmatched portfolio of digital-asset products. Binance offerings range from trading and finance to education, research, payments, institutional services, Web3 features, and more. We leverage the power of digital assets and blockchain to build an inclusive financial ecosystem to advance the freedom of money and improve financial access for people around the world. We are seeking a highly motivated and technically strong professional to support the intersection of Compliance, Legal, and AI-driven technology innovation. This role will work closely with the **Legal team, MLRO, Compliance, and cross-functional stakeholders** to enhance internal compliance processes, support policy and audit-related initiatives, and design intelligent systems that improve the efficiency and scalability of compliance and legal operations. The ideal candidate combines practical compliance or legal-tech experience with hands-on expertise in LLMs, RAG systems, and agentic AI architecture. This person should be comfortable operating in ambiguous environments, translating business and regulatory needs into technical solutions, and rapidly prototyping and deploying AI-native tools. Responsibilities Work closely with Legal, Compliance, and the MLRO to support internal compliance policy reviews, files delivery, remediation tracking, and technical support. Track compliance-related requirements for new product and service launches, and coordinate action items with relevant stakeholders. Partner with Legal and Compliance teams to identify, design, and implement AI-driven solutions that improve the efficiency and scalability of compliance and legal operations. Used the enhance intelligent systems leveraging LLMs, RAG, agentic workflows, long-term memory, tool use, subagents, and multi-agent architectures for handling law files. Develop benchmark frameworks and evaluation methodologies for legal and compliance AI systems, including benchmark dataset construction, annotation strategy design, and performance measurement. Continuously evaluate and improve systems across retrieval quality, latency, groundedness, factuality, and task success metrics. Conduct independent research and rapidly prototype new ideas to solve ambiguous legal and compliance workflow challenges. Support related administrative and operational activities in accordance with company compliance requirements. Requirements 5 years of relevant experience in compliance technology, legal technology, regulatory technology, or software/application development supporting compliance or legal functions. Strong understanding of relevant compliance rules, regulations, and day-to-day compliance operations. Bachelor’s degree required; background in AI, Computer Science, Engineering, Law, or related disciplines preferred. Hands-on experience building production RAG systems, including embeddings, vector databases, hybrid search, reranking, chunking strategies, text preprocessing, and multimodal parsing. Experience implementing advanced Agentic RAG approaches such as Self-RAG, Corrective RAG, adaptive retrieval, and multi-hop reasoning workflows. Strong understanding of LLM and agent fundamentals, including prompt engineering, context engineering, memory, planning, tool use, MCP, and multi-agent systems. Demonstrated ability to conduct independent research, solve ambiguous problems, and rapidly prototype practical solutions. Strong written and verbal communication skills. Bilingual English/Mandarin is preferred to be able to coordinate with overseas partners and stakeholders. Why Binance • Shape the future with the world’s leading blockchain ecosystem • Collaborate with world-class talent in a user-centric global organization with a flat structure • Tackle unique, fast-paced projects with autonomy in an innovative environment • Thrive in a results-driven workplace with opportunities for career growth and continuous learning • Competitive salary and company benefits • Work-from-home arrangement (the arrangement may vary depending on the work nature of the business team) Binance is committed to being an equal opportunity employer. We believe that having a diverse workforce is fundamental to our success. By submitting a job application, you confirm that you have read and agree to our Candidate Privacy Notice .53:T1ae1,

Binance is a leading global blockchain ecosystem behind the world’s largest cryptocurrency exchange by trading volume and registered users. We are trusted by 300+ million people in 100+ countries for our industry-leading security, user fund transparency, trading engine speed, deep liquidity, and an unmatched portfolio of digital-asset products. Binance offerings range from trading and finance to education, research, payments, institutional services, Web3 features, and more. We leverage the power of digital assets and blockchain to build an inclusive financial ecosystem to advance the freedom of money and improve financial access for people around the world.

We are seeking a highly motivated and technically strong professional to support the intersection of Compliance, Legal, and AI-driven technology innovation. This role will work closely with the **Legal team, MLRO, Compliance, and cross-functional stakeholders** to enhance internal compliance processes, support policy and audit-related initiatives, and design intelligent systems that improve the efficiency and scalability of compliance and legal operations.

The ideal candidate combines practical compliance or legal-tech experience with hands-on expertise in LLMs, RAG systems, and agentic AI architecture. This person should be comfortable operating in ambiguous environments, translating business and regulatory needs into technical solutions, and rapidly prototyping and deploying AI-native tools.

Responsibilities

Work closely with Legal, Compliance, and the MLRO to support internal compliance policy reviews, files delivery, remediation tracking, and technical support.

Track compliance-related requirements for new product and service launches, and coordinate action items with relevant stakeholders.

Partner with Legal and Compliance teams to identify, design, and implement AI-driven solutions that improve the efficiency and scalability of compliance and legal operations.

Used the enhance intelligent systems leveraging LLMs, RAG, agentic workflows, long-term memory, tool use, subagents, and multi-agent architectures for handling law files.

Develop benchmark frameworks and evaluation methodologies for legal and compliance AI systems, including benchmark dataset construction, annotation strategy design, and performance measurement.

Continuously evaluate and improve systems across retrieval quality, latency, groundedness, factuality, and task success metrics.

Conduct independent research and rapidly prototype new ideas to solve ambiguous legal and compliance workflow challenges.

Support related administrative and operational activities in accordance with company compliance requirements.

Requirements

5 years of relevant experience in compliance technology, legal technology, regulatory technology, or software/application development supporting compliance or legal functions.
Strong understanding of relevant compliance rules, regulations, and day-to-day compliance operations.
Bachelor’s degree required; background in AI, Computer Science, Engineering, Law, or related disciplines preferred.
Hands-on experience building production RAG systems, including embeddings, vector databases, hybrid search, reranking, chunking strategies, text preprocessing, and multimodal parsing.
Experience implementing advanced Agentic RAG approaches such as Self-RAG, Corrective RAG, adaptive retrieval, and multi-hop reasoning workflows.
Strong understanding of LLM and agent fundamentals, including prompt engineering, context engineering, memory, planning, tool use, MCP, and multi-agent systems.
Demonstrated ability to conduct independent research, solve ambiguous problems, and rapidly prototype practical solutions.
Strong written and verbal communication skills.
Bilingual English/Mandarin is preferred to be able to coordinate with overseas partners and stakeholders.

Why Binance

• Shape the future with the world’s leading blockchain ecosystem

• Collaborate with world-class talent in a user-centric global organization with a flat structure

• Tackle unique, fast-paced projects with autonomy in an innovative environment

• Thrive in a results-driven workplace with opportunities for career growth and continuous learning

• Competitive salary and company benefits

• Work-from-home arrangement (the arrangement may vary depending on the work nature of the business team)

Binance is committed to being an equal opportunity employer. We believe that having a diverse workforce is fundamental to our success.

By submitting a job application, you confirm that you have read and agree to our Candidate Privacy Notice.

54:T19b6,Toast creates technology to help restaurants and local businesses succeed in a digital world, helping business owners operate, increase sales, engage customers, and keep employees happy. The AI Agent & Experience team is Toast's internal platform team for AI. We build the infrastructure, governance, and developer tooling that makes AI a reliable, scalable force multiplier across the entire product development lifecycle. Our products include the LLM Proxy, AI Key Management, Observability pipelines, the Claude Plugin Marketplace, and autonomous agents that participate directly in the software development lifecycle. As a Senior Software Engineer on this team, you'll be hands-on building the systems that other Toast engineers depend on every day: the LLM proxy, agentic workflows, and the plugin marketplace that brings AI-native development practices to teams across the company. A day in the life (Responsibilities) Design, build, and ship features across AI platform infrastructure, including the LLM proxy, AI key management, and observability pipelines Develop and extend autonomous agents that participate in the SDLC, such as the AI Review GitHub App and Developer Platform MCP integrations Build on and contribute to the internal plugin marketplace, developing plugins that encode Toast's architectural standards and quality practices into AI assistant behavior Implement MCP (Model Context Protocol) services and service templates that make it faster and easier for teams across Toast to build AI-powered workflows Write high-quality, well-tested code and participate in design and code reviews Collaborate with engineers, product managers, and platform teams to understand developer needs and translate them into reliable platform solutions Contribute to the team's adoption of agentic development practices and tooling Leverage cutting edge AI tools to enhance your development workflow, improve velocity, and help pioneer new approaches to building - contributing to a culture of innovation and productivity across the team. What you'll need to thrive (Requirements) Bachelor’s Degree with relevant experience of 5+ years in designing and implementing scalable backend services Demonstrated experience building, deploying, or operating LLM-powered agents or AI-assisted developer tooling Familiarity with AI coding assistants (e.g., Claude Code, Cursor, GitHub Copilot) and an interest in extending them through custom plugins, skills, or hooks Experience with MCP (Model Context Protocol), tool use patterns, or similar agentic integration frameworks Working knowledge of prompt engineering and how LLM behavior responds to context and instructions Strong foundation in Java, Kotlin, or another object-oriented language Experience with distributed systems, REST APIs, and cloud-native infrastructure Strong customer empathy and a drive to make developers' lives better through reliable, well-designed platform solutions What will help you stand out Familiarity with A2A (Agent-to-Agent) protocols and multi-agent orchestration patterns Experience with machine learning concepts, model evaluation, or ML infrastructure Experience building developer experience platforms, internal tooling, or API gateways Familiarity with observability tooling for AI/LLM systems (e.g., Langfuse, DataDog) *** This is a hybrid role requiring in-office presence two days per week *** AI at Toast At Toast, one of our company values is that we're hungry to build and learn. We believe learning new AI tools empowers us to build for our customers faster, more independently, and with higher quality. We provide these tools across all disciplines, from Engineering and Product to Sales and Support, and are inspired by how our Toasters are already driving real value with them. The people who thrive here are those who embrace changes that let us build more for our customers; it’s a core part of our culture. Our Total Rewards Philosophy We strive to provide competitive compensation and benefits programs that help to attract, retain, and motivate the best and brightest people in our industry. Our total rewards package goes beyond great earnings potential and provides the means to a healthy lifestyle with the flexibility to meet Toasters’ changing needs. Learn more about our benefits at https://careers.toasttab.com/toast-benefits . #LI-Hybrid #BI-Hybrid How Toast Uses AI in its Hiring Process Throughout the hiring process, our goal is to get to know you. We use AI tools to support our recruiters and interviewers with tasks like note-taking, summarization, and documentation of interviews to ensure they can be fully focused on your conversation. All hiring decisions are made by people. We ask that you complete interviews and assessments on your own, without real-time AI tools, recording, or transcription bots, unless we tell you otherwise in advance. To learn more: https://careers.toasttab.com/ai-in-hiring Our Approach to Hybrid Working We embrace a hybrid work model that fosters in-person collaboration while valuing individual needs. Our goal is to build a strong culture of connection as we work together to empower the hospitality community, regardless of location. Please visit the Locations page on our career site to learn more about our in-office expectations by region: https://careers.toasttab.com/locations-toast People Are the Secret Ingredient in Our Recipe for Success At Toast, our employees are our secret ingredient—when they thrive, we thrive. The restaurant industry brings together people with a wide range of backgrounds, experiences, and perspectives, and we value that same breadth at Toast. We strive to build a culture grounded in authenticity, respect, and humility, where every Toaster has a real opportunity to grow, contribute, and do their best work—raising the bar in delivering exceptional experiences for our customers and each other. Apply today! Toast is committed to creating an accessible and inclusive hiring process. As part of this commitment, we strive to provide reasonable accommodations for persons with disabilities to enable them to access the hiring process. If you need an accommodation to access the job application or interview process, please contact candidateaccommodations@toasttab.com . ------ For roles in the United States, it is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.55:T1db4,

Toast creates technology to help restaurants and local businesses succeed in a digital world, helping business owners operate, increase sales, engage customers, and keep employees happy.

The AI Agent & Experience team is Toast's internal platform team for AI. We build the infrastructure, governance, and developer tooling that makes AI a reliable, scalable force multiplier across the entire product development lifecycle. Our products include the LLM Proxy, AI Key Management, Observability pipelines, the Claude Plugin Marketplace, and autonomous agents that participate directly in the software development lifecycle.

As a Senior Software Engineer on this team, you'll be hands-on building the systems that other Toast engineers depend on every day: the LLM proxy, agentic workflows, and the plugin marketplace that brings AI-native development practices to teams across the company.

A day in the life (Responsibilities)

Design, build, and ship features across AI platform infrastructure, including the LLM proxy, AI key management, and observability pipelines
Develop and extend autonomous agents that participate in the SDLC, such as the AI Review GitHub App and Developer Platform MCP integrations
Build on and contribute to the internal plugin marketplace, developing plugins that encode Toast's architectural standards and quality practices into AI assistant behavior
Implement MCP (Model Context Protocol) services and service templates that make it faster and easier for teams across Toast to build AI-powered workflows
Write high-quality, well-tested code and participate in design and code reviews
Collaborate with engineers, product managers, and platform teams to understand developer needs and translate them into reliable platform solutions
Contribute to the team's adoption of agentic development practices and tooling
Leverage cutting edge AI tools to enhance your development workflow, improve velocity, and help pioneer new approaches to building - contributing to a culture of innovation and productivity across the team.

What you'll need to thrive (Requirements)

Bachelor’s Degree with relevant experience of 5+ years in designing and implementing scalable backend services
Demonstrated experience building, deploying, or operating LLM-powered agents or AI-assisted developer tooling
Familiarity with AI coding assistants (e.g., Claude Code, Cursor, GitHub Copilot) and an interest in extending them through custom plugins, skills, or hooks
Experience with MCP (Model Context Protocol), tool use patterns, or similar agentic integration frameworks
Working knowledge of prompt engineering and how LLM behavior responds to context and instructions
Strong foundation in Java, Kotlin, or another object-oriented language
Experience with distributed systems, REST APIs, and cloud-native infrastructure
Strong customer empathy and a drive to make developers' lives better through reliable, well-designed platform solutions

What will help you stand out

Familiarity with A2A (Agent-to-Agent) protocols and multi-agent orchestration patterns
Experience with machine learning concepts, model evaluation, or ML infrastructure
Experience building developer experience platforms, internal tooling, or API gateways
Familiarity with observability tooling for AI/LLM systems (e.g., Langfuse, DataDog)

*** This is a hybrid role requiring in-office presence two days per week ***

AI at Toast

At Toast, one of our company values is that we're hungry to build and learn. We believe learning new AI tools empowers us to build for our customers faster, more independently, and with higher quality. We provide these tools across all disciplines, from Engineering and Product to Sales and Support, and are inspired by how our Toasters are already driving real value with them. The people who thrive here are those who embrace changes that let us build more for our customers; it’s a core part of our culture.

Our Total Rewards Philosophy
We strive to provide competitive compensation and benefits programs that help to attract, retain, and motivate the best and brightest people in our industry. Our total rewards package goes beyond great earnings potential and provides the means to a healthy lifestyle with the flexibility to meet Toasters’ changing needs. Learn more about our benefits at https://careers.toasttab.com/toast-benefits.

#LI-Hybrid
#BI-Hybrid

How Toast Uses AI in its Hiring Process

Throughout the hiring process, our goal is to get to know you. We use AI tools to support our recruiters and interviewers with tasks like note-taking, summarization, and documentation of interviews to ensure they can be fully focused on your conversation. All hiring decisions are made by people. We ask that you complete interviews and assessments on your own, without real-time AI tools, recording, or transcription bots, unless we tell you otherwise in advance. To learn more: https://careers.toasttab.com/ai-in-hiring

Our Approach to Hybrid Working

We embrace a hybrid work model that fosters in-person collaboration while valuing individual needs. Our goal is to build a strong culture of connection as we work together to empower the hospitality community, regardless of location. Please visit the Locations page on our career site to learn more about our in-office expectations by region: https://careers.toasttab.com/locations-toast

People Are the Secret Ingredient in Our Recipe for Success

At Toast, our employees are our secret ingredient—when they thrive, we thrive. The restaurant industry brings together people with a wide range of backgrounds, experiences, and perspectives, and we value that same breadth at Toast. We strive to build a culture grounded in authenticity, respect, and humility, where every Toaster has a real opportunity to grow, contribute, and do their best work—raising the bar in delivering exceptional experiences for our customers and each other.

Apply today!

Toast is committed to creating an accessible and inclusive hiring process. As part of this commitment, we strive to provide reasonable accommodations for persons with disabilities to enable them to access the hiring process. If you need an accommodation to access the job application or interview process, please contact candidateaccommodations@toasttab.com.

------

For roles in the United States, it is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.

56:T22d9,Coupa makes margins multiply through its community-generated AI and industry-leading total spend management platform for businesses large and small. Coupa AI is informed by trillions of dollars of direct and indirect spend data across a global network of 10M+ buyers and suppliers. We empower you with the ability to predict, prescribe, and automate smarter, more profitable business decisions to improve operating margins. Why join Coupa? Pioneering Technology: At Coupa, we're at the forefront of innovation, leveraging the latest technology to empower our customers with greater efficiency and visibility in their spend. Collaborative Culture: We value collaboration and teamwork, and our culture is driven by transparency, openness, and a shared commitment to excellence. Global Impact: Join a company where your work has a global, measurable impact on our clients, the business, and each other. Learn more on Life at Coupa blog and hear from our employees about their experiences working at Coupa. The Impact of a Senior Director, AI Delivery at Coupa: The Senior Director, AI Delivery leads the engineering organization responsible for designing, building, deploying, and operating enterprise AI solutions across the company. This leader owns the end-to-end delivery lifecycle for AI initiatives, partnering closely with business stakeholders, AI Strategy, Enterprise Architecture, Data, Infrastructure, Security, and Product teams to transform business opportunities into production ready AI capabilities. The role is accountable for execution excellence, engineering quality, delivery predictability, operational stability, governance compliance, and business value realization across a portfolio of AI agents, automations, and intelligent workflows. The focus is on delivering enterprise grade AI products that are scalable, secure, measurable, and reusable. What You'll Do: AI Delivery Leadership Lead the enterprise AI Delivery Engineering organization. Build and scale a high-performing team of AI Engineers, AI Solution Architects and AI Operations Establish delivery standards, engineering practices, and operating models for AI solution development. Own execution across multiple concurrent AI programs and business domains. Enterprise AI Solution Delivery Lead delivery of enterprise AI solutions including: AI Agents Agentic Workflows AI-powered business automations Conversational AI Knowledge assistants Retrieval-Augmented Generation (RAG) solutions Multi-agent orchestration Ensure solutions are secure, scalable, maintainable, and reusable. Engineering Excellence Establish engineering standards for: Prompt engineering Agent development Tool integration MCP implementation API integrations AI workflow orchestration Code quality Reusable frameworks Secure development practices Drive adoption of reusable AI components and engineering accelerators. Delivery & Portfolio Management Own delivery execution across the AI portfolio including: Capacity planning Prioritization Roadmaps Resource allocation Milestone management Executive reporting Risk management Delivery metrics Ensure predictable execution across multiple AI initiatives. AI Operations (Run) Establish operational excellence for production AI systems by overseeing: Production monitoring Agent health Incident management Performance optimization Model performance Prompt optimization Cost optimization AI observability Drive continuous improvement after deployment. Governance & Responsible AI Create & Manage the AI Governance council, partnering with Security, and Compliance teams to ensure: Responsible AI practices Security reviews Privacy compliance Human-in-the-loop controls Auditability Model governance Risk assessments Regulatory compliance Ensure governance is embedded throughout the delivery lifecycle. Cross-functional Leadership Partner closely with: AI Strategy Enterprise Architecture Business Systems Data & Analytics Security Business Product Owners Legal Procurement Vendor Partners Align technology delivery with business priorities. Financial & Vendor Management Manage delivery budgets and strategic partners by: Overseeing vendor performance Managing delivery costs Driving AI platform optimization Tracking consumption and FinOps metrics Measuring ROI Optimizing engineering capacity Organizational Leadership Build a world-class AI delivery organization through: Talent development Workforce planning Coaching and mentoring Organizational design Hiring Performance management Engineering culture Innovation What You Will Bring to Coupa: 12–15+ years of experience leading enterprise technology organizations. 7+ years managing engineering or enterprise application delivery / Data insights teams. Experience delivering AI, machine learning, or intelligent automation solutions at enterprise scale. Deep understanding of enterprise application ecosystems including CRM, ERP, HR, Finance, and collaboration platforms. Experience with LLMs, RAG architectures, agent frameworks, MCP, API integrations, orchestration frameworks, and enterprise AI platforms. Experience building and leading global engineering organizations. Strong executive communication and stakeholder management skills. Experience managing strategic vendors and systems integrators. Demonstrated success leading large transformation programs. Preferred Experience Experience with Google Gemini, Vertex AI, apigee, Anthropic Claude ecosystems. Experience integrating AI into enterprise platforms such as Salesforce, Netsuite, Zuora, or similar. Knowledge of Responsible AI, AI governance, and enterprise security. Experience implementing AI observability, AI testing, and operational monitoring frameworks. Familiarity with cloud-native architectures, DevOps, CI/CD, and modern software engineering practices. The successful candidate’s starting salary will be determined based on permissible, non-discriminatory factors such as skills, experience, and geographic location. At Coupa, we are 100% committed to creating and nurturing an environment that supports your physical, mental, and community well-being. We offer a comprehensive suite of benefits designed to help you and your family thrive: Global Well-Being Perks (For All Employees Globally) Global Wellness Days: Enjoy two designated, company-wide paid wellness days off each year (typically the first Friday in March and the last Friday in September) so the entire global team can unplug, step away, and recharge together . Birthday Time-Off: Celebrate your day! Coupa provides a paid day off on your birthday or another day of your choice within your birthday month . Volunteer Time Off (VTO): Giving back is in our DNA. We offer 40 hours of paid VTO annually to support the community initiatives and volunteer programs you are passionate about . Employee Assistance Program (EAP): Access free, confidential, 24/7/365 counseling and resources for emotional support, work-life solutions, financial advice, legal guidance, and support for new parents . Business Travel Protection: Travel with peace of mind. Zurich Travel Assist provides medical, safety, pre-trip planning, and emergency support during any business travel . Referral Bonus Program: Share the Coupa experience! Receive generous monetary referral bonuses when you successfully refer talented friends or acquaintances who are hired into open roles . Please note: In addition to these global well-being perks, Coupa offers highly competitive, location-specific benefits packages (which include comprehensive medical/dental insurance, retirement/pension plans, and life or accident protection) tailored to your primary work location. Your recruiter will walk you through the specific regional benefits package for this role during the interview process. Coupa complies with relevant laws and regulations regarding equal opportunity and offers a welcoming and inclusive work environment. Decisions related to hiring, compensation, training, or evaluating performance are made fairly, and we provide equal employment opportunities to all qualified candidates and employees. Please be advised that inquiries or resumes from recruiters will not be accepted. By submitting your application, you acknowledge that you have read Coupa’s Privacy Policy and understand that Coupa receives/collects your application, including your personal data, for the purposes of managing Coupa's ongoing recruitment and placement activities, including for employment purposes in the event of a successful application and for notification of future job opportunities if you did not succeed the first time. You will find more details about how your application is processed, the purposes of processing, and how long we retain your application in our Privacy Policy.57:T2a4b,

Coupa makes margins multiply through its community-generated AI and industry-leading total spend management platform for businesses large and small. Coupa AI is informed by trillions of dollars of direct and indirect spend data across a global network of 10M+ buyers and suppliers. We empower you with the ability to predict, prescribe, and automate smarter, more profitable business decisions to improve operating margins.

Why join Coupa?

Pioneering Technology: At Coupa, we're at the forefront of innovation, leveraging the latest technology to empower our customers with greater efficiency and visibility in their spend.
Collaborative Culture: We value collaboration and teamwork, and our culture is driven by transparency, openness, and a shared commitment to excellence.
Global Impact: Join a company where your work has a global, measurable impact on our clients, the business, and each other.

Learn more on Life at Coupa blog and hear from our employees about their experiences working at Coupa.

The Impact of a Senior Director, AI Delivery at Coupa:

The Senior Director, AI Delivery leads the engineering organization responsible for designing, building, deploying, and operating enterprise AI solutions across the company. This leader owns the end-to-end delivery lifecycle for AI initiatives, partnering closely with business stakeholders, AI Strategy, Enterprise Architecture, Data, Infrastructure, Security, and Product teams to transform business opportunities into production ready AI capabilities.

The role is accountable for execution excellence, engineering quality, delivery predictability, operational stability, governance compliance, and business value realization across a portfolio of AI agents, automations, and intelligent workflows. The focus is on delivering enterprise grade AI products that are scalable, secure, measurable, and reusable.

What You'll Do:

AI Delivery Leadership

Lead the enterprise AI Delivery Engineering organization.

Build and scale a high-performing team of AI Engineers, AI Solution Architects and AI Operations

Establish delivery standards, engineering practices, and operating models for AI solution development.

Own execution across multiple concurrent AI programs and business domains.

Enterprise AI Solution Delivery

Lead delivery of enterprise AI solutions including:

AI Agents

Agentic Workflows

AI-powered business automations

Conversational AI

Knowledge assistants

Retrieval-Augmented Generation (RAG) solutions

Multi-agent orchestration

Ensure solutions are secure, scalable, maintainable, and reusable.

Engineering Excellence

Establish engineering standards for:

Prompt engineering

Agent development

Tool integration

MCP implementation

API integrations

AI workflow orchestration

Code quality

Reusable frameworks

Secure development practices

Drive adoption of reusable AI components and engineering accelerators.

Delivery & Portfolio Management

Own delivery execution across the AI portfolio including:

Capacity planning

Prioritization

Roadmaps

Resource allocation

Milestone management

Executive reporting

Risk management

Delivery metrics

Ensure predictable execution across multiple AI initiatives.

AI Operations (Run)

Establish operational excellence for production AI systems by overseeing:

Production monitoring

Agent health

Incident management

Performance optimization

Model performance

Prompt optimization

Cost optimization

AI observability

Drive continuous improvement after deployment.

Governance & Responsible AI

Create & Manage the AI Governance council, partnering with Security, and Compliance teams to ensure:

Responsible AI practices

Security reviews

Privacy compliance

Human-in-the-loop controls

Auditability

Model governance

Risk assessments

Regulatory compliance

Ensure governance is embedded throughout the delivery lifecycle.

Cross-functional Leadership

Partner closely with:

AI Strategy

Enterprise Architecture

Business Systems

Data & Analytics

Security

Business Product Owners

Legal

Procurement

Vendor Partners

Align technology delivery with business priorities.

Financial & Vendor Management

Manage delivery budgets and strategic partners by:

Overseeing vendor performance

Managing delivery costs

Driving AI platform optimization

Tracking consumption and FinOps metrics

Measuring ROI

Optimizing engineering capacity

Organizational Leadership

Build a world-class AI delivery organization through:

Talent development

Workforce planning

Coaching and mentoring

Organizational design

Hiring

Performance management

Engineering culture

Innovation

What You Will Bring to Coupa:

12–15+ years of experience leading enterprise technology organizations.

7+ years managing engineering or enterprise application delivery / Data insights teams.

Experience delivering AI, machine learning, or intelligent automation solutions at enterprise scale.

Deep understanding of enterprise application ecosystems including CRM, ERP, HR, Finance, and collaboration platforms.

Experience with LLMs, RAG architectures, agent frameworks, MCP, API integrations, orchestration frameworks, and enterprise AI platforms.

Experience building and leading global engineering organizations.

Strong executive communication and stakeholder management skills.

Experience managing strategic vendors and systems integrators.

Demonstrated success leading large transformation programs.

Preferred Experience

Experience with Google Gemini, Vertex AI, apigee, Anthropic Claude ecosystems.

Experience integrating AI into enterprise platforms such as Salesforce, Netsuite, Zuora, or similar.

Knowledge of Responsible AI, AI governance, and enterprise security.

Experience implementing AI observability, AI testing, and operational monitoring frameworks.

Familiarity with cloud-native architectures, DevOps, CI/CD, and modern software engineering practices.

The successful candidate’s starting salary will be determined based on permissible, non-discriminatory factors such as skills, experience, and geographic location.

At Coupa, we are 100% committed to creating and nurturing an environment that supports your physical, mental, and community well-being. We offer a comprehensive suite of benefits designed to help you and your family thrive:

Global Well-Being Perks (For All Employees Globally)

Global Wellness Days: Enjoy two designated, company-wide paid wellness days off each year (typically the first Friday in March and the last Friday in September) so the entire global team can unplug, step away, and recharge together .
Birthday Time-Off: Celebrate your day! Coupa provides a paid day off on your birthday or another day of your choice within your birthday month .
Volunteer Time Off (VTO): Giving back is in our DNA. We offer 40 hours of paid VTO annually to support the community initiatives and volunteer programs you are passionate about .
Employee Assistance Program (EAP): Access free, confidential, 24/7/365 counseling and resources for emotional support, work-life solutions, financial advice, legal guidance, and support for new parents .
Business Travel Protection: Travel with peace of mind. Zurich Travel Assist provides medical, safety, pre-trip planning, and emergency support during any business travel .
Referral Bonus Program: Share the Coupa experience! Receive generous monetary referral bonuses when you successfully refer talented friends or acquaintances who are hired into open roles .

Please note: In addition to these global well-being perks, Coupa offers highly competitive, location-specific benefits packages (which include comprehensive medical/dental insurance, retirement/pension plans, and life or accident protection) tailored to your primary work location. Your recruiter will walk you through the specific regional benefits package for this role during the interview process.

Coupa complies with relevant laws and regulations regarding equal opportunity and offers a welcoming and inclusive work environment. Decisions related to hiring, compensation, training, or evaluating performance are made fairly, and we provide equal employment opportunities to all qualified candidates and employees.

Please be advised that inquiries or resumes from recruiters will not be accepted.

By submitting your application, you acknowledge that you have read Coupa’s Privacy Policy and understand that Coupa receives/collects your application, including your personal data, for the purposes of managing Coupa's ongoing recruitment and placement activities, including for employment purposes in the event of a successful application and for notification of future job opportunities if you did not succeed the first time. You will find more details about how your application is processed, the purposes of processing, and how long we retain your application in our Privacy Policy.

58:T191a,About Fluidstack We exist to make humanity more free. For most of human history, you farmed or you starved. Technology gave people more time for the things they wanted to do, instead of things they had to do. Powerful AI will be the biggest lever for human choice we've ever built - but only if models are aligned with what humanity actually wants. There are groups building AI who don't share these goals. Whoever deploys frontier compute infrastructure fastest will decide whether AI expands human freedom or shrinks it. We're singularly focused on delivering 10 to 100s of GWs of compute faster than anyone else, rethinking every layer of the stack. We acquire power, design and build data centers, and operate them - with teams spanning hardware and software. Speed and scale are our key differentiators. Come be a part of building civilization-scale infrastructure for AI. We hire people who care deeply about this problem space. If that is you, please apply! How We Operate Be a barrel. Full autonomy. Own things end to end, take on scope without being asked, no permission required to operate outside your core role. Insane urgency. We drive everything forward as fast as possible. Reason from first principles. Challenge every assumption. Zero analogy thinking, no egos, the best idea wins. Love of the game. The frontier of AI is the most interesting problem of our time. We put in long hours at high intensity to push the frontier forward. Build something that actually matters. If you're going to spend your time, spend it on something that matters to the world. The Decision Team Examples of key problems the team is working on Automate the delivery of gigawatts. Every process that takes AI infrastructure from land to live compute becomes software: schedules, decisions, and todos generated from a live knowledge graph instead of chased by hand. Forward-deploy beside the experts. Product teams sit with quality managers, sourcing leads, and deployment engineers on factory floors and sites, and turn their judgment into systems that reach every unit. Deliver every supercomputer faster than the last. Dozens of concurrent projects feed one graph, so every lesson learned at one site becomes a preventive check at all of them. Role Scope Build the hiring machine as software. Hiring is the company's biggest bottleneck. Every role, seat, and interview loop already lives in one graph, and you take it the rest of the way: the recruiting system synced both directions, interviewer supply and load visible to every hiring manager, and onboarding generated the moment an offer is signed, so day one starts with the job instead of the checklist. Build the cash view the company runs on: today no system derives near term spend, so finance forecasts by hand and misses what construction already committed. You turn purchase orders, contract terms, and build milestones into an order schedule, a cash demand curve by month, and a payment schedule that recompute as procurement and construction move. Build one capital pipeline from approved budget to released capital to spent dollar, gated on build milestones the delivery system already verifies, with the proof chain behind every financing drawdown assembled automatically, so releasing nine figures takes a day of judgment and funding follows construction within days. Parse every MSA, change order, and side letter into obligations, prices, deadlines, penalties, and SLAs, so a delivery lead gets what we owe a customer at a site with the clause attached, a change order arrives with its legal and finance checks done, and the corpus of executed deals prices the next negotiation. Embed forward-deployed with finance, treasury, accounting, legal, and people teams through a close, a drawdown, a negotiation, and a hiring loop, and turn their approvals, contract terms, screens, and reconciliations into structured data and generated work, including a continuous close where capitalization and depreciation post as events the moment they happen. What We're Looking For The below is a starting point. We always make space for exceptional people, so if you don't fit this role exactly, tell us where you would . You've shipped production code in Go, Python, or TypeScript, and you pick up whatever language the problem demands. You've built real features on LLM APIs (OpenAI, Anthropic, or open-weight models), MCP servers, and agentic frameworks. You work daily with AI coding tools like Claude Code and Cursor, and you get agents doing useful work autonomously alongside you. You identify problems, design the solution, and ship it without waiting for direction or approval. You've moved fast under deadline while leaving foundations that other engineers extended after you moved on. You've earned credibility with experts outside engineering: finance leads, lawyers, recruiters, or auditors, and driven adoption of your software inside their real deadlines, like a working close calendar or an offer that has to go out today. Your product taste shows in what you've shipped: a controller, a recruiter, or a lawyer calls your interface obvious, and the workflow matches how their work actually happens. Bonus: ERP and fixed asset accounting. FP&A, cash forecasting, and capital planning. Structured finance or treasury operations. Contract lifecycle systems. ATS and HRIS integrations (Ashby, Greenhouse, Rippling, Workday). LLM extraction over legal and financial documents. Benefits: Competitive total compensation package (cash + equity) Health, dental, and vision insurance Retirement plan Generous PTO policy We are committed to pay equity and transparency. Fluidstack is an Equal Employment Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability and protected veterans’ status, or any other characteristic protected by law. Fluidstack will consider for employment qualified applicants with arrest and conviction records pursuant to applicable law. You will receive a confirmation email once your application has successfully been accepted. If there is an error with your submission and you did not receive a confirmation email, please email careers@fluidstack.io with your resume/CV, the role you've applied for, and the date you submitted your application-- someone from our recruiting team will be in touch.59:T2197,

About Fluidstack

We hire people who care deeply about this problem space. If that is you, please apply!

How We Operate

Be a barrel. Full autonomy. Own things end to end, take on scope without being asked, no permission required to operate outside your core role.
Insane urgency. We drive everything forward as fast as possible.
Reason from first principles. Challenge every assumption. Zero analogy thinking, no egos, the best idea wins.
Love of the game. The frontier of AI is the most interesting problem of our time. We put in long hours at high intensity to push the frontier forward.
Build something that actually matters. If you're going to spend your time, spend it on something that matters to the world.

The Decision Team

Examples of key problems the team is working on

Automate the delivery of gigawatts. Every process that takes AI infrastructure from land to live compute becomes software: schedules, decisions, and todos generated from a live knowledge graph instead of chased by hand.
Forward-deploy beside the experts. Product teams sit with quality managers, sourcing leads, and deployment engineers on factory floors and sites, and turn their judgment into systems that reach every unit.
Deliver every supercomputer faster than the last. Dozens of concurrent projects feed one graph, so every lesson learned at one site becomes a preventive check at all of them.

Role Scope

Build the hiring machine as software. Hiring is the company's biggest bottleneck. Every role, seat, and interview loop already lives in one graph, and you take it the rest of the way: the recruiting system synced both directions, interviewer supply and load visible to every hiring manager, and onboarding generated the moment an offer is signed, so day one starts with the job instead of the checklist.
Build the cash view the company runs on: today no system derives near term spend, so finance forecasts by hand and misses what construction already committed. You turn purchase orders, contract terms, and build milestones into an order schedule, a cash demand curve by month, and a payment schedule that recompute as procurement and construction move.
Build one capital pipeline from approved budget to released capital to spent dollar, gated on build milestones the delivery system already verifies, with the proof chain behind every financing drawdown assembled automatically, so releasing nine figures takes a day of judgment and funding follows construction within days.
Parse every MSA, change order, and side letter into obligations, prices, deadlines, penalties, and SLAs, so a delivery lead gets what we owe a customer at a site with the clause attached, a change order arrives with its legal and finance checks done, and the corpus of executed deals prices the next negotiation.
Embed forward-deployed with finance, treasury, accounting, legal, and people teams through a close, a drawdown, a negotiation, and a hiring loop, and turn their approvals, contract terms, screens, and reconciliations into structured data and generated work, including a continuous close where capitalization and depreciation post as events the moment they happen.

What We're Looking For

The below is a starting point. We always make space for exceptional people, so if you don't fit this role exactly, tell us where you would.

You've shipped production code in Go, Python, or TypeScript, and you pick up whatever language the problem demands.
You've built real features on LLM APIs (OpenAI, Anthropic, or open-weight models), MCP servers, and agentic frameworks.
You work daily with AI coding tools like Claude Code and Cursor, and you get agents doing useful work autonomously alongside you.
You identify problems, design the solution, and ship it without waiting for direction or approval.
You've moved fast under deadline while leaving foundations that other engineers extended after you moved on.
You've earned credibility with experts outside engineering: finance leads, lawyers, recruiters, or auditors, and driven adoption of your software inside their real deadlines, like a working close calendar or an offer that has to go out today.
Your product taste shows in what you've shipped: a controller, a recruiter, or a lawyer calls your interface obvious, and the workflow matches how their work actually happens.
Bonus: ERP and fixed asset accounting. FP&A, cash forecasting, and capital planning. Structured finance or treasury operations. Contract lifecycle systems. ATS and HRIS integrations (Ashby, Greenhouse, Rippling, Workday). LLM extraction over legal and financial documents.

Benefits:

Competitive total compensation package (cash + equity)
Health, dental, and vision insurance
Retirement plan
Generous PTO policy

We are committed to pay equity and transparency.

5a:T20b3, Join our mission to provide governments with exceptional experiences so they can do the same for their communities! What do we do?💥 We empower governments to deliver exceptional citizen experiences. Check out our ‘About Us’ page for a deep dive into our product and what makes us exceptional. About Clariti Clariti builds community development software for cities and counties: permitting, licensing, plan review, and the workflows that let local governments serve their residents. We are in the middle of an AI-native transformation. Our platform is being built by AI-native pods, and the delivery model behind it assumes a 4x to 6x reduction in implementation time versus traditional government software projects. About the role Engineering at Clariti runs on our Agentic SDLC framework: agent specs, reusable prompts, orchestration templates, and eval harnesses that let small pods deliver at multiples of traditional velocity. The Applied AI pod owns that framework and is now extending it beyond engineering: into our Professional Services delivery practice and into the Clariti AI harness, the platform of connectors, skills, and guardrails that lets non-technical employees across Sales, CX, Finance, People, and PS use AI safely on real work. You are the senior engineer on this pod. Think of the job as DevOps for AI: you do not ship product features, you ship the infrastructure that makes everyone else faster. Where a DevOps engineer builds pipelines, golden paths, and observability for code, you build them for AI: the connector layer, the eval harnesses, the orchestration runtime, the cost and quality telemetry, and the guardrails that make agent output trustworthy enough for government software. You will work alongside a Technical Product Manager who owns the roadmap. You own how it gets built, and much of what gets built, because on a pod this small the line between architecture and implementation is yours to draw. What you will do: Build and evolve the Agentic SDLC framework. Design and implement the agent workflows, orchestration templates, and reusable components the build pods run on. Harden what exists, extend what is missing, and keep the framework fast as model capabilities and our delivery patterns change. Build the connector layer. Design, implement, and operate MCP servers and integrations into our core systems so agents and non-technical employees can act on real company data with correct permissions. Treat connectors as production software: versioned, tested, monitored, least-privilege by default. Make evals the backbone. Build and maintain the eval harnesses that score agent output automatically: regression suites for prompts and workflows, quality gates in CI, and the scoring infrastructure that tells us whether a change to a model, prompt, or workflow made things better or worse. If we cannot measure it, we cannot scale it. Own AI observability and cost telemetry. Instrument token spend, latency, eval pass rates, and usage across every production agent workflow. Build the dashboards and alerts that turn "AI is expensive and mysterious" into a managed system with unit economics per workflow. Engineer the guardrails. Implement the permissioning, audit trails, versioning, and output controls that let agent-assisted work stand up in a government context, where an artifact can end up in front of a planning commission. Make the safe path the default path in code, not in policy documents. Ship enablement infrastructure. Build the skill and template libraries, onboarding flows, and self-serve tooling that take a non-technical employee from zero to producing real work with AI, and the feedback loops that route their usage data back into the platform roadmap. What you bring 6+ years as a software engineer shipping production systems, with at least 1 to 2 years building LLM-powered or agentic systems that real users depend on, not prototypes. Strong general engineering fundamentals: you are a senior developer first and an AI specialist second. Distributed systems, API design, CI/CD, and cloud infrastructure are home territory. Hands-on depth in the current agentic stack: agent frameworks and coding agents (Claude Code, LangGraph, or equivalents), MCP or comparable tool protocols, structured outputs, and eval-driven development. You have opinions about context management and can defend them with data. A platform temperament: you measure your success by other teams' throughput, you write documentation people actually use, and you would rather delete code than defend it. Comfort operating with a small blast radius and high autonomy: this is a pod of few with a company-wide mandate, not a large team with narrow lanes. Nice to have Experience in regulated or public-sector software, where auditability and defensibility of outputs matter. Prior DevOps, platform engineering, or internal developer platform ownership; you have lived the difference between building a tool and driving its adoption. Experience instrumenting and optimizing LLM cost and quality at scale: model routing, caching, prompt compression, fine-tuning trade-offs. How success is measured 90 days: you know the Agentic SDLC framework end to end, how the build pods use it day to day, where it is strong, and where it breaks, and you are shipping to it without regression to pod velocity. You have taken over operational ownership of the existing connectors and eval harnesses from the founding team, and your first improvement to the framework, scoped with the Pod Lead against what the pods actually need, is live. 12 months: the framework is measurably faster and more reliable than the day you joined, with automated eval gates on every production agent workflow and cost and quality telemetry per workflow driving routing and optimization decisions. The connector layer covers our core systems and runs like production infrastructure. The Clariti AI harness serves a majority of non-engineering staff weekly, with uptime and support load a pod of few can sustain . What’s in it for you? 🫵 We invest in and empower our team members with competitive compensation packages, well deserved time off and benefits to keep you and your family healthy! * 💰 The base salary range for this role is expected to be between $103,000- $160,000 based on the candidate’s skills, experience, and qualifications while considering internal pay equity and our broader pay philosophy. 💰 If you have questions about compensation as we move through the process, we’re happy to discuss further. *Benefits depend on employment type (full-time, part-time, contract, etc). Things to Note 📝 Background checks - Because our customers trust us with sensitive information, we require all successful candidates to undergo comprehensive background checks before joining our team. We focus strictly on global sanctions and criminal offences that are directly relevant to employment at Clariti, and follow all applicable privacy and human rights legislation. Travel - Although we operate as a remote company, all roles are expected to participate in occasional travel for in-person company-wide or departmental meetings, typically 1-2 times per year. Additional travel requirements specific to the role, if any, will be outlined in the job description. We're committed to building an inclusive culture where our team members take ownership over projects, tasks, and outcomes; bring a growth mindset to drive continuous learning and self-development; have the ability to communicate courageously in a direct but respectful way; and are customer-focused by keeping the customer at the heart of decision-making. It’s the diversity of our team that helps us make better decisions, by leveraging the diversity in thought & experience across to create impactful solutions as we explore new paths & challenges as we grow. We’re working to create a workplace and team that is as diverse as the communities we serve. We welcome and encourage candidates of all backgrounds to apply. Questions? We are here to help If you require accommodations in completing an application, interviewing, completing any pre-employment testing, or otherwise participating in our hiring process for any reason, please direct your questions to hr@claritisoftware.com and we’ll be happy to support you.5b:T2afb,

Join our mission to provide governments with exceptional experiences so they can do the same for their communities!

What do we do?💥

We empower governments to deliver exceptional citizen experiences.

Check out our ‘About Us’ page for a deep dive into our product and what makes us exceptional.

About Clariti

Clariti builds community development software for cities and counties: permitting, licensing, plan review, and the workflows that let local governments serve their residents. We are in the middle of an AI-native transformation. Our platform is being built by AI-native pods, and the delivery model behind it assumes a 4x to 6x reduction in implementation time versus traditional government software projects.

About the role

Engineering at Clariti runs on our Agentic SDLC framework: agent specs, reusable prompts, orchestration templates, and eval harnesses that let small pods deliver at multiples of traditional velocity. The Applied AI pod owns that framework and is now extending it beyond engineering: into our Professional Services delivery practice and into the Clariti AI harness, the platform of connectors, skills, and guardrails that lets non-technical employees across Sales, CX, Finance, People, and PS use AI safely on real work.

You are the senior engineer on this pod. Think of the job as DevOps for AI: you do not ship product features, you ship the infrastructure that makes everyone else faster. Where a DevOps engineer builds pipelines, golden paths, and observability for code, you build them for AI: the connector layer, the eval harnesses, the orchestration runtime, the cost and quality telemetry, and the guardrails that make agent output trustworthy enough for government software.

You will work alongside a Technical Product Manager who owns the roadmap. You own how it gets built, and much of what gets built, because on a pod this small the line between architecture and implementation is yours to draw.

What you will do:

Build and evolve the Agentic SDLC framework. Design and implement the agent workflows, orchestration templates, and reusable components the build pods run on. Harden what exists, extend what is missing, and keep the framework fast as model capabilities and our delivery patterns change.
Build the connector layer. Design, implement, and operate MCP servers and integrations into our core systems so agents and non-technical employees can act on real company data with correct permissions. Treat connectors as production software: versioned, tested, monitored, least-privilege by default.
Make evals the backbone. Build and maintain the eval harnesses that score agent output automatically: regression suites for prompts and workflows, quality gates in CI, and the scoring infrastructure that tells us whether a change to a model, prompt, or workflow made things better or worse. If we cannot measure it, we cannot scale it.
Own AI observability and cost telemetry. Instrument token spend, latency, eval pass rates, and usage across every production agent workflow. Build the dashboards and alerts that turn "AI is expensive and mysterious" into a managed system with unit economics per workflow.
Engineer the guardrails. Implement the permissioning, audit trails, versioning, and output controls that let agent-assisted work stand up in a government context, where an artifact can end up in front of a planning commission. Make the safe path the default path in code, not in policy documents.
Ship enablement infrastructure. Build the skill and template libraries, onboarding flows, and self-serve tooling that take a non-technical employee from zero to producing real work with AI, and the feedback loops that route their usage data back into the platform roadmap.

What you bring

6+ years as a software engineer shipping production systems, with at least 1 to 2 years building LLM-powered or agentic systems that real users depend on, not prototypes.
Strong general engineering fundamentals: you are a senior developer first and an AI specialist second. Distributed systems, API design, CI/CD, and cloud infrastructure are home territory.
Hands-on depth in the current agentic stack: agent frameworks and coding agents (Claude Code, LangGraph, or equivalents), MCP or comparable tool protocols, structured outputs, and eval-driven development. You have opinions about context management and can defend them with data.
A platform temperament: you measure your success by other teams' throughput, you write documentation people actually use, and you would rather delete code than defend it.
Comfort operating with a small blast radius and high autonomy: this is a pod of few with a company-wide mandate, not a large team with narrow lanes.

Nice to have

Experience in regulated or public-sector software, where auditability and defensibility of outputs matter.
Prior DevOps, platform engineering, or internal developer platform ownership; you have lived the difference between building a tool and driving its adoption.
Experience instrumenting and optimizing LLM cost and quality at scale: model routing, caching, prompt compression, fine-tuning trade-offs.

How success is measured

90 days: you know the Agentic SDLC framework end to end, how the build pods use it day to day, where it is strong, and where it breaks, and you are shipping to it without regression to pod velocity. You have taken over operational ownership of the existing connectors and eval harnesses from the founding team, and your first improvement to the framework, scoped with the Pod Lead against what the pods actually need, is live.
12 months: the framework is measurably faster and more reliable than the day you joined, with automated eval gates on every production agent workflow and cost and quality telemetry per workflow driving routing and optimization decisions. The connector layer covers our core systems and runs like production infrastructure. The Clariti AI harness serves a majority of non-engineering staff weekly, with uptime and support load a pod of few can sustain.

What’s in it for you?🫵

We invest in and empower our team members with competitive compensation packages, well deserved time off and benefits to keep you and your family healthy! *

💰 The base salary range for this role is expected to be between $103,000- $160,000 based on the candidate’s skills, experience, and qualifications while considering internal pay equity and our broader pay philosophy. 💰

If you have questions about compensation as we move through the process, we’re happy to discuss further.

*Benefits depend on employment type (full-time, part-time, contract, etc).

Things to Note 📝

Background checks - Because our customers trust us with sensitive information, we require all successful candidates to undergo comprehensive background checks before joining our team. We focus strictly on global sanctions and criminal offences that are directly relevant to employment at Clariti, and follow all applicable privacy and human rights legislation.

Travel- Although we operate as a remote company, all roles are expected to participate in occasional travel for in-person company-wide or departmental meetings, typically 1-2 times per year. Additional travel requirements specific to the role, if any, will be outlined in the job description.

We're committed to building an inclusive culture where our team members take ownership over projects, tasks, and outcomes; bring a growth mindset to drive continuous learning and self-development; have the ability to communicate courageously in a direct but respectful way; and are customer-focused by keeping the customer at the heart of decision-making. It’s the diversity of our team that helps us make better decisions, by leveraging the diversity in thought & experience across to create impactful solutions as we explore new paths & challenges as we grow. We’re working to create a workplace and team that is as diverse as the communities we serve. We welcome and encourage candidates of all backgrounds to apply.

Questions? We are here to help

If you require accommodations in completing an application, interviewing, completing any pre-employment testing, or otherwise participating in our hiring process for any reason, please direct your questions to hr@claritisoftware.com and we’ll be happy to support you.

5c:T2305, About Artefact Artefact is a next-generation data and AI consulting firm dedicated to accelerating the adoption of data and AI to create measurable business impact across the full enterprise value chain. We sit at the intersection of consulting, data science, AI technologies, data engineering, and digital transformation. We do not just advise — we build, implement, and deliver results our clients can measure. Our teams bring together consultants, data scientists, data engineers, AI engineers, analysts, and digital experts to solve complex business challenges with pragmatic, production-ready solutions. As Artefact continues to grow globally, we are building a team of entrepreneurial data and AI talent who can help clients move beyond experimentation and into scalable, governed, value-generating AI adoption. The Role: Artefact is looking for a Senior Deployed AI Engineer specialized in the Anthropic (Claude) ecosystem: an engineer who works embedded with our clients and takes AI products from idea to production. You will design and build the interfaces, services, and agentic systems at the heart of our client work, such as conversational applications over enterprise data, multi-step agents that automate business workflows, and the retrieval and data pipelines that support them. You will own your components end to end: the front end, the service behind it, the data and retrieval pipelines feeding it, the deployment, and the evaluations proving it works. This role combines deep, certified expertise in the Anthropic ecosystem (Claude models, the Claude Platform, and its agentic tooling) with the ability to deliver end to end. Beyond your platform specialization, you will be expected to work confidently across the full delivery lifecycle — full-stack development, data engineering, cloud infrastructure, evaluation, and client communication. You will work closely with our clients, with direct exposure from the start, and you will support the professional development of the engineers around you. Develop , schedule, and monitor data pipelines using Apache Airflow. Collaborate with data analysts, scientists, and engineering teams to ensure reliable data delivery. Optimize ETL processes for performance, reliability, and scalability. Maintain and improve data architecture, ensuring data integrity and security. Troubleshoot and resolve pipeline failures and performance bottlenecks. Provide technical leadership and guidance in the implementation of technical solutions, ensuring alignment with industry standards and compliance requirements. Document architectural decisions , strategies, and implementation guidelines for future reference. Lead by example , promoting engineering excellence and a culture of continuous improvement. Coach and upskill team members by providing technical guidance, pair programming, design reviews, and knowledge sharing. What You'll Do Build Full-Stack AI Applications, End to End You will build AI products across the entire stack, from interface to infrastructure. Develop user-facing interfaces in TypeScript/React and the backend services and APIs behind them in Python or Node. Implement agentic behavior: orchestration, tool and function calling, memory, and guardrails. Build retrieval-augmented generation (RAG) pipelines: ingestion, chunking, embeddings, vector and hybrid search. Connect AI systems to enterprise data and applications via APIs, semantic layers, and protocols such as MCP. Go Deep on the Anthropic Platform You will be the team's reference for the Anthropic platform. Design and build agentic systems with the Claude API and Claude Agent SDK: tool use, extended thinking, prompt caching, structured outputs, and citations. Build and deploy cloud-hosted agents with Claude Managed Agents: sandboxed code execution, memory, checkpointing, webhooks, multi-agent orchestration, and end-to-end tracing. Build and integrate Model Context Protocol (MCP) servers and clients to connect Claude to enterprise systems and data. Make Claude Code a core part of delivery — skills, subagents, hooks — and help clients adopt agentic development workflows. Deploy Claude in enterprise environments via the Claude API, Google Cloud Vertex AI, or Amazon Bedrock, selecting the right model (Opus, Sonnet, Haiku) for each cost, latency, and quality trade-off. Track Anthropic's releases closely and translate new capabilities into client value quickly. Make AI Systems Production-Grade Our standard is production quality: systems that are reliable, monitored, and maintainable. Write evaluation suites and regression tests for LLM-powered features, and monitor cost, latency, and quality in production. Apply solid engineering practice: version control, code review, automated testing, CI/CD, and observability. Deploy on cloud infrastructure (GCP, Azure, or AWS) using containers, serverless, and infrastructure-as-code. Build and maintain the data pipelines that feed AI systems, across warehouses, lakehouses, and vector stores. Work AI-Natively and Client-Facing Our engineers work AI-natively and represent Artefact directly with clients. Use agentic coding tools (Claude Code, Gemini CLI, Codex, Cursor) daily, with good judgment about verification and review. Communicate progress, trade-offs, and blockers clearly to clients and project leads. Support pre-sales when needed: scope solutions, build demos, and estimate effort with our partnership and consulting teams. Mentor junior engineers and contribute to internal accelerators, reusable components, and engineering standards. What We're Looking For Required Experience 3–5 years of experience in software engineering or data engineering, with extensive hands-on use of AI tools and LLM-based development over the past year (professional projects, internal initiatives, or substantial personal builds). Professional English proficiency (C1/C2 minimum) — mandatory. You will work daily with international clients and colleagues. Strong hands-on experience with the Anthropic ecosystem: Claude API, Claude Agent SDK or Claude Code, and MCP — ideally with experience taking at least one solution to production. Strong programming skills in Python and TypeScript/JavaScript, and experience building and consuming APIs. Experience with front-end development (React or similar) and at least one backend framework. Hands-on experience with RAG, embeddings, and vector search, and with at least one agentic framework (Claude Agent SDK, LangGraph/LangChain). Working experience with at least one cloud platform (GCP, AWS, or Azure); running Claude via Vertex AI or Amazon Bedrock is a plus. Fluency with agentic coding tools such as Claude Code, Gemini CLI, Codex, or Cursor. Experience building and maintaining data pipelines. Bachelor's or Master's degree in computer science, engineering, or a related field, or equivalent practical experience. Certifications An Anthropic certification is a strong differentiator at application. If you do not hold one yet, obtaining it within your first 2 months in the role is a requirement — Artefact sponsors the exam and gives you time to prepare Claude Certified Developer – Foundations (Anthropic Certification Program), or a higher-level Claude Certified Architect credential. Anthropic Academy completion certificates (Claude API, MCP, Claude Code) are valued as supporting evidence. Preferred Experience Experience with MCP servers, multi-agent patterns, or LLM evaluation tooling (LangSmith, Langfuse, promptfoo). Experience with Terraform or CI/CD pipelines. Contributions to the MCP ecosystem, published MCP servers, or open-source agentic tooling. Key Capabilities A strong candidate will bring: Deep expertise in the Anthropic platform, combined with breadth across the full stack Owns features end to end, from interface to infrastructure Cares about evaluation and reliability, not just the happy path Communicates clearly with clients in demos, documents, and code review Client-facing mindset: understands client needs and translates business requirements into technical solutions Learns new tools and models fast, and shares what works What We Offer: Meal (VR) Free Office (work from home or anywhere you want!) Gympass Insurance: Life, Health, and Dental Bi-monthly Meetings (our “Get Together” where we meet to be together, with workshops, lectures, training, and a happy hour!) Woba (you can book coworking spaces anywhere you want!) Semi-annual evaluations (with opportunities for promotion) Why you should join us Artefact is the place to be: come and build the future of marketing Progress: every day offers new challenges and new opportunities to learn Culture: join the best team you could ever imagine Entrepreneurship: you will be joining a team of driven entrepreneurs. We won’t give up until we make a huge dent in this industry! Come join us! 5d:T2ac0,

About Artefact

Artefact is a next-generation data and AI consulting firm dedicated to accelerating the adoption of data and AI to create measurable business impact across the full enterprise value chain.
We sit at the intersection of consulting, data science, AI technologies, data engineering, and digital transformation. We do not just advise — we build, implement, and deliver results our clients can measure. Our teams bring together consultants, data scientists, data engineers, AI engineers, analysts, and digital experts to solve complex business challenges with pragmatic, production-ready solutions.
As Artefact continues to grow globally, we are building a team of entrepreneurial data and AI talent who can help clients move beyond experimentation and into scalable, governed, value-generating AI adoption.

The Role:

Artefact is looking for a Senior Deployed AI Engineer specialized in the Anthropic (Claude) ecosystem: an engineer who works embedded with our clients and takes AI products from idea to production.

You will design and build the interfaces, services, and agentic systems at the heart of our client work, such as conversational applications over enterprise data, multi-step agents that automate business workflows, and the retrieval and data pipelines that support them. You will own your components end to end: the front end, the service behind it, the data and retrieval pipelines feeding it, the deployment, and the evaluations proving it works.

This role combines deep, certified expertise in the Anthropic ecosystem (Claude models, the Claude Platform, and its agentic tooling) with the ability to deliver end to end. Beyond your platform specialization, you will be expected to work confidently across the full delivery lifecycle — full-stack development, data engineering, cloud infrastructure, evaluation, and client communication.

You will work closely with our clients, with direct exposure from the start, and you will support the professional development of the engineers around you.

Develop, schedule, and monitor data pipelines using Apache Airflow.
Collaborate with data analysts, scientists, and engineering teams to ensure reliable data delivery.
Optimize ETL processes for performance, reliability, and scalability.
Maintain and improve data architecture, ensuring data integrity and security.
Troubleshoot and resolve pipeline failures and performance bottlenecks.
Provide technical leadership and guidance in the implementation of technical solutions, ensuring alignment with industry standards and compliance requirements.
Document architectural decisions, strategies, and implementation guidelines for future reference.
Lead by example, promoting engineering excellence and a culture of continuous improvement.
Coach and upskill team members by providing technical guidance, pair programming, design reviews, and knowledge sharing.

What You'll Do
Build Full-Stack AI Applications, End to End

You will build AI products across the entire stack, from interface to infrastructure.

Develop user-facing interfaces in TypeScript/React and the backend services and APIs behind them in Python or Node.
Implement agentic behavior: orchestration, tool and function calling, memory, and guardrails.
Build retrieval-augmented generation (RAG) pipelines: ingestion, chunking, embeddings, vector and hybrid search.
Connect AI systems to enterprise data and applications via APIs, semantic layers, and protocols such as MCP.

Go Deep on the Anthropic Platform
You will be the team's reference for the Anthropic platform.

Design and build agentic systems with the Claude API and Claude Agent SDK: tool use, extended thinking, prompt caching, structured outputs, and citations.
Build and deploy cloud-hosted agents with Claude Managed Agents: sandboxed code execution, memory, checkpointing, webhooks, multi-agent orchestration, and end-to-end tracing.
Build and integrate Model Context Protocol (MCP) servers and clients to connect Claude to enterprise systems and data.
Make Claude Code a core part of delivery — skills, subagents, hooks — and help clients adopt agentic development workflows.
Deploy Claude in enterprise environments via the Claude API, Google Cloud Vertex AI, or Amazon Bedrock, selecting the right model (Opus, Sonnet, Haiku) for each cost, latency, and quality trade-off.
Track Anthropic's releases closely and translate new capabilities into client value quickly.

Make AI Systems Production-Grade
Our standard is production quality: systems that are reliable, monitored, and maintainable.

Write evaluation suites and regression tests for LLM-powered features, and monitor cost, latency, and quality in production.
Apply solid engineering practice: version control, code review, automated testing, CI/CD, and observability.
Deploy on cloud infrastructure (GCP, Azure, or AWS) using containers, serverless, and infrastructure-as-code.
Build and maintain the data pipelines that feed AI systems, across warehouses, lakehouses, and vector stores.

Work AI-Natively and Client-Facing
Our engineers work AI-natively and represent Artefact directly with clients.

Use agentic coding tools (Claude Code, Gemini CLI, Codex, Cursor) daily, with good judgment about verification and review.
Communicate progress, trade-offs, and blockers clearly to clients and project leads.
Support pre-sales when needed: scope solutions, build demos, and estimate effort with our partnership and consulting teams.
Mentor junior engineers and contribute to internal accelerators, reusable components, and engineering standards.

What We're Looking For
Required Experience

3–5 years of experience in software engineering or data engineering, with extensive hands-on use of AI tools and LLM-based development over the past year (professional projects, internal initiatives, or substantial personal builds).
Professional English proficiency (C1/C2 minimum) — mandatory. You will work daily with international clients and colleagues.
Strong hands-on experience with the Anthropic ecosystem: Claude API, Claude Agent SDK or Claude Code, and MCP — ideally with experience taking at least one solution to production.
Strong programming skills in Python and TypeScript/JavaScript, and experience building and consuming APIs.
Experience with front-end development (React or similar) and at least one backend framework.
Hands-on experience with RAG, embeddings, and vector search, and with at least one agentic framework (Claude Agent SDK, LangGraph/LangChain).
Working experience with at least one cloud platform (GCP, AWS, or Azure); running Claude via Vertex AI or Amazon Bedrock is a plus.
Fluency with agentic coding tools such as Claude Code, Gemini CLI, Codex, or Cursor.
Experience building and maintaining data pipelines.
Bachelor's or Master's degree in computer science, engineering, or a related field, or equivalent practical experience.

Certifications
An Anthropic certification is a strong differentiator at application. If you do not hold one yet, obtaining it within your first 2 months in the role is a requirement — Artefact sponsors the exam and gives you time to prepare

Claude Certified Developer – Foundations (Anthropic Certification Program), or a higher-level Claude Certified Architect credential.
Anthropic Academy completion certificates (Claude API, MCP, Claude Code) are valued as supporting evidence.

Preferred Experience

Experience with MCP servers, multi-agent patterns, or LLM evaluation tooling (LangSmith, Langfuse, promptfoo).
Experience with Terraform or CI/CD pipelines.
Contributions to the MCP ecosystem, published MCP servers, or open-source agentic tooling.

Key Capabilities
A strong candidate will bring:

Deep expertise in the Anthropic platform, combined with breadth across the full stack
Owns features end to end, from interface to infrastructure
Cares about evaluation and reliability, not just the happy path
Communicates clearly with clients in demos, documents, and code review
Client-facing mindset: understands client needs and translates business requirements into technical solutions
Learns new tools and models fast, and shares what works

What We Offer:

Meal (VR)
Free Office (work from home or anywhere you want!)
Gympass
Insurance: Life, Health, and Dental
Bi-monthly Meetings (our “Get Together” where we meet to be together, with workshops, lectures, training, and a happy hour!)
Woba (you can book coworking spaces anywhere you want!)
Semi-annual evaluations (with opportunities for promotion)

Why you should join us

Artefact is the place to be: come and build the future of marketing
Progress: every day offers new challenges and new opportunities to learn
Culture: join the best team you could ever imagine
Entrepreneurship: you will be joining a team of driven entrepreneurs. We won’t give up until we make a huge dent in this industry!

Come join us!

5e:T22c1,Airbyte is the data and action layer for AI agents. We give agents fast, accurate, authenticated access to business data across hundreds of sources, so they can discover the entities that matter, reason over real-time context, and take action in the systems they read from, not just observe them. We started as the open-source standard for data movement and proved the economics of data integration at scale: hundreds of connectors, thousands of companies, and, since 2020, have raised $181M from leading investors including Benchmark, Accel, Altimeter, Coatue, and Y Combinator. As our CEO Michel Tricot puts it, "the last ten years were all about structured data. The future is all about context." We're now building that context infrastructure for production-grade agents on the same open foundation, as agents become the primary consumers of enterprise data. Our mission is unchanged: make data available and actionable to everyone, everywhere. That everyone now includes AI agents. The Role: Airbyte is building the runtime that powers production-grade AI agents. This is not an ML research or prompt engineering role. We’re looking for an experienced backend or platform engineer who has built complex production systems and has also worked hands-on with AI agents, orchestration, retrieval, or LLM-powered applications. Our AI Runtime sits between language models and the enterprise systems agents need to understand and act on. It is responsible for assembling the right context, identifying entities across systems, selecting tools and connectors, coordinating execution, validating evidence, enforcing permissions, and handling failures safely. A user may ask something simple like: “Investigate this customer and tell me what changed.” Behind that request, the runtime may need to identify the customer across multiple systems, retrieve fresh data, determine which tools to call, coordinate multiple steps, validate the result, and return an answer with evidence the user can trust. That infrastructure is what you’ll help build. We’re still early. The architecture is taking shape, but there is significant room to influence how the runtime is designed, how developers interact with it, and how we make agentic systems reliable in production. What You’ll Do: Build the Runtime Design and implement the orchestration layer that turns natural-language intent into reliable, multi-step execution. Build systems for routing, state management, retries, failure handling, and long-running agent workflows. Develop systems for entity resolution, context assembly, connector orchestration, and evidence retrieval. Build reusable Skills that encapsulate business workflows and domain-specific capabilities. Coordinate connectors, APIs, tools, deterministic logic, retrieval systems, and multiple language models. Build Reliable AI Infrastructure Design systems that retrieve and assemble the right enterprise context at runtime. Build evidence-backed reasoning with citations and traceability that users can inspect and verify. Implement permission models, freshness validation, and action policies for production environments. Build evaluation, replay, observability, and debugging systems that help us understand why agents succeed or fail. Improve the reliability of systems where model outputs may be probabilistic, but execution cannot be. Own Products End to End Take ambiguous product ideas from prototype through production. Own features across architecture, implementation, rollout, and iteration. Work directly with Product, Design, Sales Engineering, Customer Success, and customers to understand real-world problems. Write high-leverage code that creates reusable infrastructure for future product areas. Experiment with new agent architectures while maintaining production-grade reliability. What You’ll Need: You may be a strong fit if you are fundamentally a backend or platform engineer who has also spent meaningful time building production AI systems. 7+ years of software engineering experience building and operating production systems. Strong backend engineering fundamentals and experience designing distributed systems. Experience building complex systems involving APIs, asynchronous workflows, concurrency, queues, state, retries, or distributed execution. Hands-on experience building production applications using LLMs, agents, RAG, tool calling, MCP, or similar technologies. Experience with orchestration systems, workflow engines, developer platforms, or infrastructure products. Strong system design skills and the ability to reason about reliability, scale, observability, and failure modes. Ability to move quickly from prototype to production without sacrificing engineering quality. Strong product instincts and comfort operating in ambiguous, 0-to-1 environments. Exceptional written communication skills. A strong bias toward ownership and shipping. Nice To Have Experience building agent infrastructure, agent platforms, or evaluation systems. Familiarity with orchestration technologies such as LangGraph, Temporal, MCP, or similar. Experience with retrieval systems, vector search, search infrastructure, or knowledge graphs. Experience building developer tools or platform infrastructure. Experience with data infrastructure such as Kafka, Iceberg, Postgres, Spark, or modern data warehouses. Experience working on early-stage products where you helped define architecture and technical direction. What Success Looks Like Success is not measured by the sophistication of the prompts. It is measured by whether the runtime becomes more reliable, observable, and trustworthy over time. You’ll build systems that: Assemble the right context and choose the correct connectors, tools, and Skills. Execute multi-step workflows reliably. Recover gracefully when something fails. Produce evidence-backed answers with citations and traceability. Enforce permissions, freshness guarantees, and action policies. Learn from failures through replay, evaluation, and observability. Allow users to focus on outcomes rather than coordinating individual tools themselves. As the runtime evolves, developers and users should need to think less about the underlying tools and more about what they want to accomplish. The runtime should handle the complexity underneath. Why This Role Matters AI agents work impressively in demos. Making them reliable in production is much harder. The difficult problems are often not the model itself. They are retrieving the right context, connecting to real enterprise systems, maintaining permissions, coordinating multiple actions, recovering from failures, and determining whether an answer can actually be trusted. Airbyte already connects to hundreds of business systems. We’re building the runtime that allows agents to use that infrastructure intelligently and safely. If we succeed, developers will not need to rebuild orchestration, retrieval, permissions, evaluation, and enterprise connectivity every time they build a new agent. Location Onsite 4 days/week in San Francisco, CA Why You'll Love Working at Airbyte: At Airbyte, we believe great work happens when people feel supported, trusted, and empowered to grow. Our market-leading Total Rewards package is designed to help you thrive professionally and personally. Our benefits and perks include: Flexible PTO with a culture that encourages at least 25 days off annually 12-16 weeks fully paid parental leave for all parents Comprehensive medical, dental, and vision coverage for employees and dependents 401(k) retirement plan Professional development budget, conference sponsorship, and book reimbursement Commuter benefits and monthly internet reimbursement Breakfast and lunch in our San Francisco office A collaborative, in-person culture focused on learning, growth, and impact If you find this role exciting, we encourage you to apply even if you think you don’t meet all of the requirements! We are not accepting agency submissions or recruiting firm support for this role. Unsolicited resumes will not be considered. Airbyte is an equal opportunity employer that does not discriminate on the basis of actual or perceived race, creed, color, religion, national origin, ancestry, age, physical or mental disability, pregnancy, genetic information, sex, sexual orientation, gender identity or expression, marital status, familial status, domestic violence victim status, veteran or military status, or any other legally recognized protected basis under federal, state or local laws. Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. Airbyte is committed to providing reasonable accommodations for qualified individuals with disabilities in our job application procedures. Please let us know if you need assistance or accommodations due to a disability.5f:T31b6,

Airbyte is the data and action layer for AI agents. We give agents fast, accurate, authenticated access to business data across hundreds of sources, so they can discover the entities that matter, reason over real-time context, and take action in the systems they read from, not just observe them.

We started as the open-source standard for data movement and proved the economics of data integration at scale: hundreds of connectors, thousands of companies, and, since 2020, have raised $181M from leading investors including Benchmark, Accel, Altimeter, Coatue, and Y Combinator. As our CEO Michel Tricot puts it, "the last ten years were all about structured data. The future is all about context." We're now building that context infrastructure for production-grade agents on the same open foundation, as agents become the primary consumers of enterprise data.

Our mission is unchanged: make data available and actionable to everyone, everywhere. That everyone now includes AI agents.

The Role:

Airbyte is building the runtime that powers production-grade AI agents.

This is not an ML research or prompt engineering role. We’re looking for an experienced backend or platform engineer who has built complex production systems and has also worked hands-on with AI agents, orchestration, retrieval, or LLM-powered applications.

Our AI Runtime sits between language models and the enterprise systems agents need to understand and act on. It is responsible for assembling the right context, identifying entities across systems, selecting tools and connectors, coordinating execution, validating evidence, enforcing permissions, and handling failures safely.

A user may ask something simple like:

“Investigate this customer and tell me what changed.”

Behind that request, the runtime may need to identify the customer across multiple systems, retrieve fresh data, determine which tools to call, coordinate multiple steps, validate the result, and return an answer with evidence the user can trust.

That infrastructure is what you’ll help build.

We’re still early. The architecture is taking shape, but there is significant room to influence how the runtime is designed, how developers interact with it, and how we make agentic systems reliable in production.

What You’ll Do:

Build the Runtime

Design and implement the orchestration layer that turns natural-language intent into reliable, multi-step execution.
Build systems for routing, state management, retries, failure handling, and long-running agent workflows.
Develop systems for entity resolution, context assembly, connector orchestration, and evidence retrieval.
Build reusable Skills that encapsulate business workflows and domain-specific capabilities.
Coordinate connectors, APIs, tools, deterministic logic, retrieval systems, and multiple language models.

Build Reliable AI Infrastructure

Design systems that retrieve and assemble the right enterprise context at runtime.
Build evidence-backed reasoning with citations and traceability that users can inspect and verify.
Implement permission models, freshness validation, and action policies for production environments.
Build evaluation, replay, observability, and debugging systems that help us understand why agents succeed or fail.
Improve the reliability of systems where model outputs may be probabilistic, but execution cannot be.

Own Products End to End

Take ambiguous product ideas from prototype through production.
Own features across architecture, implementation, rollout, and iteration.
Work directly with Product, Design, Sales Engineering, Customer Success, and customers to understand real-world problems.
Write high-leverage code that creates reusable infrastructure for future product areas.
Experiment with new agent architectures while maintaining production-grade reliability.

What You’ll Need:

You may be a strong fit if you are fundamentally a backend or platform engineer who has also spent meaningful time building production AI systems.

7+ years of software engineering experience building and operating production systems.
Strong backend engineering fundamentals and experience designing distributed systems.
Experience building complex systems involving APIs, asynchronous workflows, concurrency, queues, state, retries, or distributed execution.
Hands-on experience building production applications using LLMs, agents, RAG, tool calling, MCP, or similar technologies.
Experience with orchestration systems, workflow engines, developer platforms, or infrastructure products.
Strong system design skills and the ability to reason about reliability, scale, observability, and failure modes.
Ability to move quickly from prototype to production without sacrificing engineering quality.
Strong product instincts and comfort operating in ambiguous, 0-to-1 environments.
Exceptional written communication skills.
A strong bias toward ownership and shipping.

Nice To Have

Experience building agent infrastructure, agent platforms, or evaluation systems.
Familiarity with orchestration technologies such as LangGraph, Temporal, MCP, or similar.
Experience with retrieval systems, vector search, search infrastructure, or knowledge graphs.
Experience building developer tools or platform infrastructure.
Experience with data infrastructure such as Kafka, Iceberg, Postgres, Spark, or modern data warehouses.
Experience working on early-stage products where you helped define architecture and technical direction.

What Success Looks Like

Success is not measured by the sophistication of the prompts. It is measured by whether the runtime becomes more reliable, observable, and trustworthy over time.

You’ll build systems that:

Assemble the right context and choose the correct connectors, tools, and Skills.
Execute multi-step workflows reliably.
Recover gracefully when something fails.
Produce evidence-backed answers with citations and traceability.
Enforce permissions, freshness guarantees, and action policies.
Learn from failures through replay, evaluation, and observability.
Allow users to focus on outcomes rather than coordinating individual tools themselves.

As the runtime evolves, developers and users should need to think less about the underlying tools and more about what they want to accomplish. The runtime should handle the complexity underneath.

Why This Role Matters

AI agents work impressively in demos. Making them reliable in production is much harder.

The difficult problems are often not the model itself. They are retrieving the right context, connecting to real enterprise systems, maintaining permissions, coordinating multiple actions, recovering from failures, and determining whether an answer can actually be trusted.

Airbyte already connects to hundreds of business systems. We’re building the runtime that allows agents to use that infrastructure intelligently and safely.

If we succeed, developers will not need to rebuild orchestration, retrieval, permissions, evaluation, and enterprise connectivity every time they build a new agent.

Location

Onsite 4 days/week in San Francisco, CA

Why You'll Love Working at Airbyte:

At Airbyte, we believe great work happens when people feel supported, trusted, and empowered to grow. Our market-leading Total Rewards package is designed to help you thrive professionally and personally. Our benefits and perks include:

Flexible PTO with a culture that encourages at least 25 days off annually
12-16 weeks fully paid parental leave for all parents
Comprehensive medical, dental, and vision coverage for employees and dependents
401(k) retirement plan
Professional development budget, conference sponsorship, and book reimbursement
Commuter benefits and monthly internet reimbursement
Breakfast and lunch in our San Francisco office
A collaborative, in-person culture focused on learning, growth, and impact

If you find this role exciting, we encourage you to apply even if you think you don’t meet all of the requirements!

We are not accepting agency submissions or recruiting firm support for this role. Unsolicited resumes will not be considered.

Airbyte is an equal opportunity employer that does not discriminate on the basis of actual or perceived race, creed, color, religion, national origin, ancestry, age, physical or mental disability, pregnancy, genetic information, sex, sexual orientation, gender identity or expression, marital status, familial status, domestic violence victim status, veteran or military status, or any other legally recognized protected basis under federal, state or local laws. Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

Airbyte is committed to providing reasonable accommodations for qualified individuals with disabilities in our job application procedures. Please let us know if you need assistance or accommodations due to a disability.

60:T1728,For more than 30 years, SonicWall has delivered a partner-first unified cybersecurity portfolio that helps SMBs, MSPs, and IT teams consolidate network security, endpoint security, security service edge (SSE), and threat response across hybrid environments. By bringing connected technology, real-time threat intelligence, automation, and expert services together, SonicWall helps organizations simplify security operations, achieve better security outcomes, and lower total cost of ownership. Backed by a global ecosystem of more than 17,000 partners, SonicWall delivers enterprise-grade protection built for SMB and mid-market realities. For more information, visit www.sonicwall.com or follow us on X, LinkedIn , Facebook , and Instagram . Position Summary We are hiring a Software Dev Engineer to design, build, test, and deploy AI-powered applications. You will work across the full application lifecycle — from architecture and implementation through automated testing, CI/CD deployment, and production monitoring — building features that put large language models and agentic tooling to work inside SonicWall's internal systems. This is a hands-on engineering role for someone with 6–8 years of professional software development experience who is comfortable owning services end to end: writing production-quality code, integrating LLM and agentic APIs, standing up reliable data and retrieval pipelines, and shipping through a disciplined test-and-release process. You will collaborate closely with senior engineers, product stakeholders, and platform teams to turn requirements into dependable, well-tested applications. Key Responsibilities Develop AI applications: Design and build features and services that use LLM and agentic capabilities — prompting, tool/function calling, retrieval-augmented generation (RAG), and orchestration — to solve real internal business problems. Write production code: Implement clean, maintainable, well-documented backend and integration code in C# and .NET, following established architecture and coding standards. Test thoroughly: Write unit, integration, and end-to-end tests; build and maintain automated test suites; validate output quality and correctness for AI-driven features before release. Deploy and operate: Package, deploy, and support applications through CI/CD pipelines; monitor performance, reliability, and cost in production; troubleshoot and resolve issues quickly. Integrate with enterprise systems: Connect applications to internal and third-party systems and data sources via REST/JSON APIs, MCP servers, and the iPaaS layer, respecting access controls and data-handling rules. Engineer for quality and safety: Implement evaluation, guardrails, logging, and observability so AI features behave predictably and safely at production scale. Collaborate cross-functionally: Work with senior engineers, product owners, and business stakeholders to translate requirements into technical designs, and participate actively in code review. Document and hand-off: Produce clear technical documentation and runbooks so applications are supportable by the broader team. Required Qualifications Experience: 6–8 years of professional software development experience, including building and shipping production applications. Programming: Strong proficiency in C# and .NET, plus solid REST/JSON API design skills. AI application development: Hands-on experience building applications that integrate LLMs — prompting, function/tool calling, RAG, and use of orchestration frameworks such as the Model Context Protocol (MCP). Testing: Practical experience with automated testing — unit, integration, and end-to-end — and a disciplined approach to quality assurance. Deployment: Experience with CI/CD pipelines (Azure DevOps), containerization (Docker), and deploying and operating applications in the cloud. Databases: Working experience with SQL and NoSQL databases (e.g., MongoDB); comfortable design schemas and writing efficient queries. Cloud fundamentals: Solid experience with Microsoft Azure, version control (Git), and observability/monitoring practices. Problem solving: Demonstrated ability to break down ambiguous requirements into a working, tested, and deployable solution. Communication: Strong written and verbal English; able to explain technical trade-offs clearly to both engineers and non-technical stakeholders. Preferred Qualifications Agentic tooling: Experience with the Model Context Protocol (MCP), LangChain, LangGraph, or comparable agentic orchestration frameworks. AI platform: Familiarity with Azure AI Foundry (good to have). LLM platforms: Practical experience with Anthropic Claude, Claude Code, or the Claude Agent SDK, or equivalent LLM provider platforms. Vector search: Experience with vector databases (e.g., Pinecone, Weaviate) and retrieval pipeline design. Containers & orchestration: Familiarity with Kubernetes (e.g., Azure Kubernetes Service) or similar container-orchestration platforms. Enterprise applications: Familiarity with Salesforce, Snowflake, Oracle EBS, or similar enterprise application landscapes. Industry background: Experience in security, SaaS, or B2B technology sectors. #LI-DS9 #LI-Bengaluru #LI-Hybrid #LI-GenerativeAI #AI-Agentic SonicWall is an equal opportunity employer. We are committed to creating a diverse environment and are an equal opportunity employer. All qualified applicants receive consideration for employment without regard to race, color, ethnicity, religion, sex, gender, gender identity and expression, sexual orientation, national origin, disability, age, marital status, veteran status, pregnancy, or any other basis prohibited by applicable law. At SonicWall, we pride ourselves on recruiting a diverse mix of talented people and providing active security solutions in 100+ countries. Applicant Privacy Notice61:T4fa5,

For more than 30 years, SonicWall has delivered a partner-first unified cybersecurity portfolio that helps SMBs, MSPs, and IT teams consolidate network security, endpoint security, security service edge (SSE), and threat response across hybrid environments. By bringing connected technology, real-time threat intelligence, automation, and expert services together, SonicWall helps organizations simplify security operations, achieve better security outcomes, and lower total cost of ownership. Backed by a global ecosystem of more than 17,000 partners, SonicWall delivers enterprise-grade protection built for SMB and mid-market realities.

For more information, visit www.sonicwall.com or follow us on X, LinkedIn, Facebook, and Instagram.

Position Summary

We are hiring a Software Dev Engineer to design, build, test, and deploy AI-powered applications. You will work across the full application lifecycle — from architecture and implementation through automated testing, CI/CD deployment, and production monitoring — building features that put large language models and agentic tooling to work inside SonicWall's internal systems.

This is a hands-on engineering role for someone with 6–8 years of professional software development experience who is comfortable owning services end to end: writing production-quality code, integrating LLM and agentic APIs, standing up reliable data and retrieval pipelines, and shipping through a disciplined test-and-release process. You will collaborate closely with senior engineers, product stakeholders, and platform teams to turn requirements into dependable, well-tested applications.

Key Responsibilities

Develop AI applications: Design and build features and services that use LLM and agentic capabilities — prompting, tool/function calling, retrieval-augmented generation (RAG), and orchestration — to solve real internal business problems.
Write production code: Implement clean, maintainable, well-documented backend and integration code in C# and .NET, following established architecture and coding standards.
Test thoroughly: Write unit, integration, and end-to-end tests; build and maintain automated test suites; validate output quality and correctness for AI-driven features before release.
Deploy and operate: Package, deploy, and support applications through CI/CD pipelines; monitor performance, reliability, and cost in production; troubleshoot and resolve issues quickly.
Integrate with enterprise systems: Connect applications to internal and third-party systems and data sources via REST/JSON APIs, MCP servers, and the iPaaS layer, respecting access controls and data-handling rules.
Engineer for quality and safety: Implement evaluation, guardrails, logging, and observability so AI features behave predictably and safely at production scale.
Collaborate cross-functionally: Work with senior engineers, product owners, and business stakeholders to translate requirements into technical designs, and participate actively in code review.
Document and hand-off: Produce clear technical documentation and runbooks so applications are supportable by the broader team.

Required Qualifications

Experience: 6–8 years of professional software development experience, including building and shipping production applications.
Programming: Strong proficiency in C# and .NET, plus solid REST/JSON API design skills.
AI application development: Hands-on experience building applications that integrate LLMs — prompting, function/tool calling, RAG, and use of orchestration frameworks such as the Model Context Protocol (MCP).
Testing: Practical experience with automated testing — unit, integration, and end-to-end — and a disciplined approach to quality assurance.
Deployment: Experience with CI/CD pipelines (Azure DevOps), containerization (Docker), and deploying and operating applications in the cloud.
Databases: Working experience with SQL and NoSQL databases (e.g., MongoDB); comfortable design schemas and writing efficient queries.
Cloud fundamentals: Solid experience with Microsoft Azure, version control (Git), and observability/monitoring practices.
Problem solving: Demonstrated ability to break down ambiguous requirements into a working, tested, and deployable solution.
Communication: Strong written and verbal English; able to explain technical trade-offs clearly to both engineers and non-technical stakeholders.

Preferred Qualifications

Agentic tooling: Experience with the Model Context Protocol (MCP), LangChain, LangGraph, or comparable agentic orchestration frameworks.
AI platform: Familiarity with Azure AI Foundry (good to have).
LLM platforms: Practical experience with Anthropic Claude, Claude Code, or the Claude Agent SDK, or equivalent LLM provider platforms.
Vector search: Experience with vector databases (e.g., Pinecone, Weaviate) and retrieval pipeline design.
Containers & orchestration: Familiarity with Kubernetes (e.g., Azure Kubernetes Service) or similar container-orchestration platforms.
Enterprise applications: Familiarity with Salesforce, Snowflake, Oracle EBS, or similar enterprise application landscapes.
Industry background: Experience in security, SaaS, or B2B technology sectors.

#LI-DS9

#LI-Bengaluru

#LI-Hybrid

#LI-GenerativeAI

#AI-Agentic

SonicWall is an equal opportunity employer.

We are committed to creating a diverse environment and are an equal opportunity employer. All qualified applicants receive consideration for employment without regard to race, color, ethnicity, religion, sex, gender, gender identity and expression, sexual orientation, national origin, disability, age, marital status, veteran status, pregnancy, or any other basis prohibited by applicable law.

At SonicWall, we pride ourselves on recruiting a diverse mix of talented people and providing active security solutions in 100+ countries.

Applicant Privacy Notice

62:T191a,About Fluidstack We exist to make humanity more free. For most of human history, you farmed or you starved. Technology gave people more time for the things they wanted to do, instead of things they had to do. Powerful AI will be the biggest lever for human choice we've ever built - but only if models are aligned with what humanity actually wants. There are groups building AI who don't share these goals. Whoever deploys frontier compute infrastructure fastest will decide whether AI expands human freedom or shrinks it. We're singularly focused on delivering 10 to 100s of GWs of compute faster than anyone else, rethinking every layer of the stack. We acquire power, design and build data centers, and operate them - with teams spanning hardware and software. Speed and scale are our key differentiators. Come be a part of building civilization-scale infrastructure for AI. We hire people who care deeply about this problem space. If that is you, please apply! How We Operate Be a barrel. Full autonomy. Own things end to end, take on scope without being asked, no permission required to operate outside your core role. Insane urgency. We drive everything forward as fast as possible. Reason from first principles. Challenge every assumption. Zero analogy thinking, no egos, the best idea wins. Love of the game. The frontier of AI is the most interesting problem of our time. We put in long hours at high intensity to push the frontier forward. Build something that actually matters. If you're going to spend your time, spend it on something that matters to the world. The Decision Team Examples of key problems the team is working on Automate the delivery of gigawatts. Every process that takes AI infrastructure from land to live compute becomes software: schedules, decisions, and todos generated from a live knowledge graph instead of chased by hand. Forward-deploy beside the experts. Product teams sit with quality managers, sourcing leads, and deployment engineers on factory floors and sites, and turn their judgment into systems that reach every unit. Deliver every supercomputer faster than the last. Dozens of concurrent projects feed one graph, so every lesson learned at one site becomes a preventive check at all of them. Role Scope Build the hiring machine as software. Hiring is the company's biggest bottleneck. Every role, seat, and interview loop already lives in one graph, and you take it the rest of the way: the recruiting system synced both directions, interviewer supply and load visible to every hiring manager, and onboarding generated the moment an offer is signed, so day one starts with the job instead of the checklist. Build the cash view the company runs on: today no system derives near term spend, so finance forecasts by hand and misses what construction already committed. You turn purchase orders, contract terms, and build milestones into an order schedule, a cash demand curve by month, and a payment schedule that recompute as procurement and construction move. Build one capital pipeline from approved budget to released capital to spent dollar, gated on build milestones the delivery system already verifies, with the proof chain behind every financing drawdown assembled automatically, so releasing nine figures takes a day of judgment and funding follows construction within days. Parse every MSA, change order, and side letter into obligations, prices, deadlines, penalties, and SLAs, so a delivery lead gets what we owe a customer at a site with the clause attached, a change order arrives with its legal and finance checks done, and the corpus of executed deals prices the next negotiation. Embed forward-deployed with finance, treasury, accounting, legal, and people teams through a close, a drawdown, a negotiation, and a hiring loop, and turn their approvals, contract terms, screens, and reconciliations into structured data and generated work, including a continuous close where capitalization and depreciation post as events the moment they happen. What We're Looking For The below is a starting point. We always make space for exceptional people, so if you don't fit this role exactly, tell us where you would . You've shipped production code in Go, Python, or TypeScript, and you pick up whatever language the problem demands. You've built real features on LLM APIs (OpenAI, Anthropic, or open-weight models), MCP servers, and agentic frameworks. You work daily with AI coding tools like Claude Code and Cursor, and you get agents doing useful work autonomously alongside you. You identify problems, design the solution, and ship it without waiting for direction or approval. You've moved fast under deadline while leaving foundations that other engineers extended after you moved on. You've earned credibility with experts outside engineering: finance leads, lawyers, recruiters, or auditors, and driven adoption of your software inside their real deadlines, like a working close calendar or an offer that has to go out today. Your product taste shows in what you've shipped: a controller, a recruiter, or a lawyer calls your interface obvious, and the workflow matches how their work actually happens. Bonus: ERP and fixed asset accounting. FP&A, cash forecasting, and capital planning. Structured finance or treasury operations. Contract lifecycle systems. ATS and HRIS integrations (Ashby, Greenhouse, Rippling, Workday). LLM extraction over legal and financial documents. Benefits: Competitive total compensation package (cash + equity) Health, dental, and vision insurance Retirement plan Generous PTO policy We are committed to pay equity and transparency. Fluidstack is an Equal Employment Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability and protected veterans’ status, or any other characteristic protected by law. Fluidstack will consider for employment qualified applicants with arrest and conviction records pursuant to applicable law. You will receive a confirmation email once your application has successfully been accepted. If there is an error with your submission and you did not receive a confirmation email, please email careers@fluidstack.io with your resume/CV, the role you've applied for, and the date you submitted your application-- someone from our recruiting team will be in touch.63:T2197,

About Fluidstack

We hire people who care deeply about this problem space. If that is you, please apply!

How We Operate

Be a barrel. Full autonomy. Own things end to end, take on scope without being asked, no permission required to operate outside your core role.
Insane urgency. We drive everything forward as fast as possible.
Reason from first principles. Challenge every assumption. Zero analogy thinking, no egos, the best idea wins.
Love of the game. The frontier of AI is the most interesting problem of our time. We put in long hours at high intensity to push the frontier forward.
Build something that actually matters. If you're going to spend your time, spend it on something that matters to the world.

The Decision Team

Examples of key problems the team is working on

Automate the delivery of gigawatts. Every process that takes AI infrastructure from land to live compute becomes software: schedules, decisions, and todos generated from a live knowledge graph instead of chased by hand.
Forward-deploy beside the experts. Product teams sit with quality managers, sourcing leads, and deployment engineers on factory floors and sites, and turn their judgment into systems that reach every unit.
Deliver every supercomputer faster than the last. Dozens of concurrent projects feed one graph, so every lesson learned at one site becomes a preventive check at all of them.

Role Scope

Build the hiring machine as software. Hiring is the company's biggest bottleneck. Every role, seat, and interview loop already lives in one graph, and you take it the rest of the way: the recruiting system synced both directions, interviewer supply and load visible to every hiring manager, and onboarding generated the moment an offer is signed, so day one starts with the job instead of the checklist.
Build the cash view the company runs on: today no system derives near term spend, so finance forecasts by hand and misses what construction already committed. You turn purchase orders, contract terms, and build milestones into an order schedule, a cash demand curve by month, and a payment schedule that recompute as procurement and construction move.
Build one capital pipeline from approved budget to released capital to spent dollar, gated on build milestones the delivery system already verifies, with the proof chain behind every financing drawdown assembled automatically, so releasing nine figures takes a day of judgment and funding follows construction within days.
Parse every MSA, change order, and side letter into obligations, prices, deadlines, penalties, and SLAs, so a delivery lead gets what we owe a customer at a site with the clause attached, a change order arrives with its legal and finance checks done, and the corpus of executed deals prices the next negotiation.
Embed forward-deployed with finance, treasury, accounting, legal, and people teams through a close, a drawdown, a negotiation, and a hiring loop, and turn their approvals, contract terms, screens, and reconciliations into structured data and generated work, including a continuous close where capitalization and depreciation post as events the moment they happen.

What We're Looking For

The below is a starting point. We always make space for exceptional people, so if you don't fit this role exactly, tell us where you would.

You've shipped production code in Go, Python, or TypeScript, and you pick up whatever language the problem demands.
You've built real features on LLM APIs (OpenAI, Anthropic, or open-weight models), MCP servers, and agentic frameworks.
You work daily with AI coding tools like Claude Code and Cursor, and you get agents doing useful work autonomously alongside you.
You identify problems, design the solution, and ship it without waiting for direction or approval.
You've moved fast under deadline while leaving foundations that other engineers extended after you moved on.
You've earned credibility with experts outside engineering: finance leads, lawyers, recruiters, or auditors, and driven adoption of your software inside their real deadlines, like a working close calendar or an offer that has to go out today.
Your product taste shows in what you've shipped: a controller, a recruiter, or a lawyer calls your interface obvious, and the workflow matches how their work actually happens.
Bonus: ERP and fixed asset accounting. FP&A, cash forecasting, and capital planning. Structured finance or treasury operations. Contract lifecycle systems. ATS and HRIS integrations (Ashby, Greenhouse, Rippling, Workday). LLM extraction over legal and financial documents.

Benefits:

Competitive total compensation package (cash + equity)
Health, dental, and vision insurance
Retirement plan
Generous PTO policy

We are committed to pay equity and transparency.

64:T181f,About Fluidstack We exist to make humanity more free. For most of human history, you farmed or you starved. Technology gave people more time for the things they wanted to do, instead of things they had to do. Powerful AI will be the biggest lever for human choice we've ever built - but only if models are aligned with what humanity actually wants. There are groups building AI who don't share these goals. Whoever deploys frontier compute infrastructure fastest will decide whether AI expands human freedom or shrinks it. We're singularly focused on delivering 10 to 100s of GWs of compute faster than anyone else, rethinking every layer of the stack. We acquire power, design and build data centers, and operate them - with teams spanning hardware and software. Speed and scale are our key differentiators. Come be a part of building civilization-scale infrastructure for AI. We hire people who care deeply about this problem space. If that is you, please apply! How We Operate Be a barrel. Full autonomy. Own things end to end, take on scope without being asked, no permission required to operate outside your core role. Insane urgency. We drive everything forward as fast as possible. Reason from first principles. Challenge every assumption. Zero analogy thinking, no egos, the best idea wins. Love of the game. The frontier of AI is the most interesting problem of our time. We put in long hours at high intensity to push the frontier forward. Build something that actually matters. If you're going to spend your time, spend it on something that matters to the world. The Decision Team Examples of key problems the team is working on Automate the delivery of gigawatts. Every process that takes AI infrastructure from land to live compute becomes software: schedules, decisions, and todos generated from a live knowledge graph instead of chased by hand. Forward-deploy beside the experts. Product teams sit with quality managers, sourcing leads, and deployment engineers on factory floors and sites, and turn their judgment into systems that reach every unit. Deliver every supercomputer faster than the last. Dozens of concurrent projects feed one graph, so every lesson learned at one site becomes a preventive check at all of them. Role Scope Build the schedule that runs the portfolio: one template schedule per building type, instantiated per project, with every activity tied to the materials it consumes, so need-by dates for switchgear, transformers, and cooling fall out of the critical path instead of a spreadsheet, and a slip in permitting reprices the procurement calendar the same hour. Turn the site into structured data: work packages, contractor field entries, rack placements, cable runs, and QA inspections land as records tied to the design revision they were built against, percent complete is computed from evidence rather than reported on a call, and the customer sees delivered against committed without anyone assembling a deck. Ship change orders as a workflow, not an email chain: scope, cost, and schedule impact drafted from the design delta, routed through legal, finance, and the general contractor with the approval trail attached, and the bill of materials and schedule updated the moment the order is signed. Make the design the source of truth: ingest building information models straight into the bill of materials with a reviewable diff on every revision, generate submittal requirements per spec section, and keep sites, buildings, and data halls as one canonical record that every downstream system reads instead of copies. Ship commissioning as software: test procedures generated per asset from the design revision across all five levels, results landing as evidence against the asset they tested, energization gates that check themselves, and handover that opens on proof instead of a signature. What We're Looking For The below is a starting point. We always make space for exceptional people, so if you don't fit this role exactly, tell us where you would . You've shipped production code in Go, Python, or TypeScript, and you pick up whatever language the problem demands. You've built real features on LLM APIs (OpenAI, Anthropic, or open-weight models), MCP servers, and agentic frameworks. You work daily with AI coding tools like Claude Code and Cursor, and you get agents doing useful work autonomously alongside you. You identify problems, design the solution, and ship it without waiting for direction or approval. You've moved fast under deadline while leaving foundations that other engineers extended after you moved on. You've earned credibility with people who build physical things: construction leads, field engineers, schedulers, or commissioning agents, and driven adoption of your software inside their real deadlines. Your product taste shows in what you've shipped: interfaces field crews call obvious, and workflows that match how the work actually happens. Bonus: Construction tech or project controls (P6, schedule engines, critical path). BIM, IFC, or Revit data models. Commissioning, BMS/EPMS, or controls protocols. Document control and submittal workflows. Graph data models. Computer vision on site imagery. Benefits: Competitive total compensation package (cash + equity) Health, dental, and vision insurance Retirement plan Generous PTO policy We are committed to pay equity and transparency. Fluidstack is an Equal Employment Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability and protected veterans’ status, or any other characteristic protected by law. Fluidstack will consider for employment qualified applicants with arrest and conviction records pursuant to applicable law. You will receive a confirmation email once your application has successfully been accepted. If there is an error with your submission and you did not receive a confirmation email, please email careers@fluidstack.io with your resume/CV, the role you've applied for, and the date you submitted your application-- someone from our recruiting team will be in touch.65:T20b8,

About Fluidstack

We hire people who care deeply about this problem space. If that is you, please apply!

How We Operate

Be a barrel. Full autonomy. Own things end to end, take on scope without being asked, no permission required to operate outside your core role.
Insane urgency. We drive everything forward as fast as possible.
Reason from first principles. Challenge every assumption. Zero analogy thinking, no egos, the best idea wins.
Love of the game. The frontier of AI is the most interesting problem of our time. We put in long hours at high intensity to push the frontier forward.
Build something that actually matters. If you're going to spend your time, spend it on something that matters to the world.

The Decision Team

Examples of key problems the team is working on

Automate the delivery of gigawatts. Every process that takes AI infrastructure from land to live compute becomes software: schedules, decisions, and todos generated from a live knowledge graph instead of chased by hand.
Forward-deploy beside the experts. Product teams sit with quality managers, sourcing leads, and deployment engineers on factory floors and sites, and turn their judgment into systems that reach every unit.
Deliver every supercomputer faster than the last. Dozens of concurrent projects feed one graph, so every lesson learned at one site becomes a preventive check at all of them.

Role Scope

Build the schedule that runs the portfolio: one template schedule per building type, instantiated per project, with every activity tied to the materials it consumes, so need-by dates for switchgear, transformers, and cooling fall out of the critical path instead of a spreadsheet, and a slip in permitting reprices the procurement calendar the same hour.
Turn the site into structured data: work packages, contractor field entries, rack placements, cable runs, and QA inspections land as records tied to the design revision they were built against, percent complete is computed from evidence rather than reported on a call, and the customer sees delivered against committed without anyone assembling a deck.
Ship change orders as a workflow, not an email chain: scope, cost, and schedule impact drafted from the design delta, routed through legal, finance, and the general contractor with the approval trail attached, and the bill of materials and schedule updated the moment the order is signed.
Make the design the source of truth: ingest building information models straight into the bill of materials with a reviewable diff on every revision, generate submittal requirements per spec section, and keep sites, buildings, and data halls as one canonical record that every downstream system reads instead of copies.
Ship commissioning as software: test procedures generated per asset from the design revision across all five levels, results landing as evidence against the asset they tested, energization gates that check themselves, and handover that opens on proof instead of a signature.

What We're Looking For

The below is a starting point. We always make space for exceptional people, so if you don't fit this role exactly, tell us where you would.

You've shipped production code in Go, Python, or TypeScript, and you pick up whatever language the problem demands.
You've built real features on LLM APIs (OpenAI, Anthropic, or open-weight models), MCP servers, and agentic frameworks.
You work daily with AI coding tools like Claude Code and Cursor, and you get agents doing useful work autonomously alongside you.
You identify problems, design the solution, and ship it without waiting for direction or approval.
You've moved fast under deadline while leaving foundations that other engineers extended after you moved on.
You've earned credibility with people who build physical things: construction leads, field engineers, schedulers, or commissioning agents, and driven adoption of your software inside their real deadlines.
Your product taste shows in what you've shipped: interfaces field crews call obvious, and workflows that match how the work actually happens.
Bonus: Construction tech or project controls (P6, schedule engines, critical path). BIM, IFC, or Revit data models. Commissioning, BMS/EPMS, or controls protocols. Document control and submittal workflows. Graph data models. Computer vision on site imagery.

Benefits:

Competitive total compensation package (cash + equity)
Health, dental, and vision insurance
Retirement plan
Generous PTO policy

We are committed to pay equity and transparency.

66:T18c0,About Fluidstack We exist to make humanity more free. For most of human history, you farmed or you starved. Technology gave people more time for the things they wanted to do, instead of things they had to do. Powerful AI will be the biggest lever for human choice we've ever built - but only if models are aligned with what humanity actually wants. There are groups building AI who don't share these goals. Whoever deploys frontier compute infrastructure fastest will decide whether AI expands human freedom or shrinks it. We're singularly focused on delivering 10 to 100s of GWs of compute faster than anyone else, rethinking every layer of the stack. We acquire power, design and build data centers, and operate them - with teams spanning hardware and software. Speed and scale are our key differentiators. Come be a part of building civilization-scale infrastructure for AI. We hire people who care deeply about this problem space. If that is you, please apply! How We Operate Be a barrel. Full autonomy. Own things end to end, take on scope without being asked, no permission required to operate outside your core role. Insane urgency. We drive everything forward as fast as possible. Reason from first principles. Challenge every assumption. Zero analogy thinking, no egos, the best idea wins. Love of the game. The frontier of AI is the most interesting problem of our time. We put in long hours at high intensity to push the frontier forward. Build something that actually matters. If you're going to spend your time, spend it on something that matters to the world. The Decision Team Examples of key problems the team is working on Automate the delivery of gigawatts. Every process that takes AI infrastructure from land to live compute becomes software: schedules, decisions, and todos generated from a live knowledge graph instead of chased by hand. Forward-deploy beside the experts. Product teams sit with quality managers, sourcing leads, and deployment engineers on factory floors and sites, and turn their judgment into systems that reach every unit. Deliver every supercomputer faster than the last. Dozens of concurrent projects feed one graph, so every lesson learned at one site becomes a preventive check at all of them. Role Scope Turn the bill of materials into the order book: every template bill carries the price and lead time of each line, every project bill locks against a building, and every purchase order derives from it, so tens of billions of dollars of GPUs, optics, switchgear, and cooling are ordered from one source of truth instead of a spreadsheet. Make procurement tracking trustworthy: the rules that decide when a line is late, what its status means, and which lines sit on the critical path run in code against live vendor emails, documents, and general contractor purchase orders through the extractors you ship, so drift is caught the week it happens and the recovery plan is drafted before the vendor calls. Close the loop from receiving to payment: every delivery matches a purchase order line, every warehouse bin knows its serial numbers, cycle counts are scored against an accuracy gate, and finance three-way matches from the same record, so the question "did we get what we paid for" has one answer instead of three. Ship manufacturing as software before the factory opens: manufacturing bills of materials, engineering change notices, and the drawing tree live in one model, inspection and test plans and first article procedures generate per asset from the design revision, and a failed check opens a nonconformance with the evidence attached and rescores the vendor. Embed with sourcing leads, quality managers, factory operators, and warehouse teams, on their floors and in their vendor calls, and turn their judgment into the systems that decide the order schedule, the cash demand by month, and the payment schedule the company plans against. What We're Looking For The below is a starting point. We always make space for exceptional people, so if you don't fit this role exactly, tell us where you would . You've shipped production code in Go, Python, or TypeScript, and you pick up whatever language the problem demands. You've built real features on LLM APIs (OpenAI, Anthropic, or open-weight models), MCP servers, and agentic frameworks, and you've put document extraction into production against messy real inputs. You work daily with AI coding tools like Claude Code and Cursor, and you get agents doing useful work autonomously alongside you. You identify problems, design the solution, and ship it without waiting for direction or approval. You've moved fast under deadline while leaving data models and foundations that other engineers extended after you moved on. You've earned credibility with operators in factories, warehouses, or procurement, and driven adoption of your software inside their real deadlines, like a live production run or a quarter-end close. Your product taste shows in what you've shipped: interfaces the floor calls obvious, and workflows that match how the work actually happens. Bonus: MES, ERP, WMS, or TMS systems. BOM lineage, engineering change control, and serialized component traceability. Supplier quality (NCRs, CAPA, FAI). Three-way match and receiving reconciliation. LLM extraction over vendor documents. High-volume hardware supply chains. Benefits: Competitive total compensation package (cash + equity) Health, dental, and vision insurance Retirement plan Generous PTO policy We are committed to pay equity and transparency. Fluidstack is an Equal Employment Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability and protected veterans’ status, or any other characteristic protected by law. Fluidstack will consider for employment qualified applicants with arrest and conviction records pursuant to applicable law. You will receive a confirmation email once your application has successfully been accepted. If there is an error with your submission and you did not receive a confirmation email, please email careers@fluidstack.io with your resume/CV, the role you've applied for, and the date you submitted your application-- someone from our recruiting team will be in touch.67:T2139,

About Fluidstack

We hire people who care deeply about this problem space. If that is you, please apply!

How We Operate

Be a barrel. Full autonomy. Own things end to end, take on scope without being asked, no permission required to operate outside your core role.
Insane urgency. We drive everything forward as fast as possible.
Reason from first principles. Challenge every assumption. Zero analogy thinking, no egos, the best idea wins.
Love of the game. The frontier of AI is the most interesting problem of our time. We put in long hours at high intensity to push the frontier forward.
Build something that actually matters. If you're going to spend your time, spend it on something that matters to the world.

The Decision Team

Examples of key problems the team is working on

Automate the delivery of gigawatts. Every process that takes AI infrastructure from land to live compute becomes software: schedules, decisions, and todos generated from a live knowledge graph instead of chased by hand.
Forward-deploy beside the experts. Product teams sit with quality managers, sourcing leads, and deployment engineers on factory floors and sites, and turn their judgment into systems that reach every unit.
Deliver every supercomputer faster than the last. Dozens of concurrent projects feed one graph, so every lesson learned at one site becomes a preventive check at all of them.

Role Scope

Turn the bill of materials into the order book: every template bill carries the price and lead time of each line, every project bill locks against a building, and every purchase order derives from it, so tens of billions of dollars of GPUs, optics, switchgear, and cooling are ordered from one source of truth instead of a spreadsheet.
Make procurement tracking trustworthy: the rules that decide when a line is late, what its status means, and which lines sit on the critical path run in code against live vendor emails, documents, and general contractor purchase orders through the extractors you ship, so drift is caught the week it happens and the recovery plan is drafted before the vendor calls.
Close the loop from receiving to payment: every delivery matches a purchase order line, every warehouse bin knows its serial numbers, cycle counts are scored against an accuracy gate, and finance three-way matches from the same record, so the question "did we get what we paid for" has one answer instead of three.
Ship manufacturing as software before the factory opens: manufacturing bills of materials, engineering change notices, and the drawing tree live in one model, inspection and test plans and first article procedures generate per asset from the design revision, and a failed check opens a nonconformance with the evidence attached and rescores the vendor.
Embed with sourcing leads, quality managers, factory operators, and warehouse teams, on their floors and in their vendor calls, and turn their judgment into the systems that decide the order schedule, the cash demand by month, and the payment schedule the company plans against.

What We're Looking For

The below is a starting point. We always make space for exceptional people, so if you don't fit this role exactly, tell us where you would.

You've shipped production code in Go, Python, or TypeScript, and you pick up whatever language the problem demands.
You've built real features on LLM APIs (OpenAI, Anthropic, or open-weight models), MCP servers, and agentic frameworks, and you've put document extraction into production against messy real inputs.
You work daily with AI coding tools like Claude Code and Cursor, and you get agents doing useful work autonomously alongside you.
You identify problems, design the solution, and ship it without waiting for direction or approval.
You've moved fast under deadline while leaving data models and foundations that other engineers extended after you moved on.
You've earned credibility with operators in factories, warehouses, or procurement, and driven adoption of your software inside their real deadlines, like a live production run or a quarter-end close.
Your product taste shows in what you've shipped: interfaces the floor calls obvious, and workflows that match how the work actually happens.
Bonus: MES, ERP, WMS, or TMS systems. BOM lineage, engineering change control, and serialized component traceability. Supplier quality (NCRs, CAPA, FAI). Three-way match and receiving reconciliation. LLM extraction over vendor documents. High-volume hardware supply chains.

Benefits:

Competitive total compensation package (cash + equity)
Health, dental, and vision insurance
Retirement plan
Generous PTO policy

We are committed to pay equity and transparency.

68:T159e,🎨 About OpenArt OpenArt is an AI storytelling and visual creation platform used by millions of creators worldwide. We're building the next generation of creative tools powered by cutting-edge AI — enabling anyone to create videos, visuals, characters, and stories with unprecedented speed and imagination. We believe the future of creativity is AI-native, and we're shaping that future. 🚀 Why Join OpenArt - Ground floor of a new category — help define what "agent" means for creative work, the way Director redefined what "directing" means for video. - Small team, massive surface area — engineers own real systems end to end, not slices. - Real scale, real stakes — your agents run for millions of creators, and what you ship shows up in the product within days. - Founder-led engineering culture — both founders are technical and deeply involved in product and architecture. - AI-native product — you'll help decide how frontier models get orchestrated and exposed as experiences that feel effortless rather than technical. - High ownership, low process — we value judgment, clarity, and speed over bureaucracy. 🎯 About the Role We're hiring engineers to build the agent layer behind OpenArt's next generation of creative products, starting with Director — which lets anyone "vibe direct" a multi-minute cinematic video by chatting with AI. You'll be building the agent infrastructure that pushes experiences like this further: longer-horizon planning, tool use across multiple model vendors, memory across sessions, and the harness that keeps all of it reliable in production. This isn't a research role — it's a product-and-systems role. You'll build on and extend the agent harness (tool use, context management, multi-step planning, evaluation), develop the MCP servers and CLI tooling around it, and ship the frontend experiences that make all of it feel effortless rather than technical. You'll work closely with the founders, the agent team, and design to turn "what should this agent be able to do" into a system that actually does it — reliably, at scale. 🛠 What You'll Do - Build and extend the agent harness that powers OpenArt's conversational and autonomous creative workflows: tool use, context/memory management, multi-step planning, and sub-agent orchestration. - Build and maintain MCP servers and CLI-based agent tooling that connect our agents to internal services, external model vendors, and creative asset pipelines. - Author reusable Agent Skills and structured workflows that let agents reliably handle complex, multi-step creative tasks — scene planning, storyboarding, asset generation, revision loops. - Contribute to evaluation frameworks and observability so the team knows when an agent is working well, and can catch regressions before users do. - Ship polished, production frontend experiences (React/Next.js) for agent-driven products, end to end. - Make pragmatic trade-offs between speed, correctness, and scalability — and communicate them clearly. - Partner closely with product, design, and GTM to translate user needs into technical solutions. - Track the fast-moving agent ecosystem (MCP, Agent Skills, orchestration frameworks) and bring the best of it into OpenArt's stack. ⚙ What We're Looking For Core Requirements - 2+ years of full-stack engineering experience shipping production software. - Hands-on experience building LLM-powered agents or applications that have reached real users — not just prototypes or demos. - Working familiarity with agent harness concepts: tool use, context/memory management, multi-step planning, or sub-agent orchestration. - Solid data modeling and schema design skills for real product use cases. - A strong AI-assisted coding practice — you use tools like Claude Code, Codex, or Cursor as a core part of how you build, and have real opinions about what makes them work well. - Product sense — you think about what makes an agent experience feel trustworthy and delightful, not just functional. - Clear communication — you can explain agent behavior and technical trade-offs to teammates who aren't engineers. Nice to Have - Experience authoring reusable Agent Skills or structured agent workflows. - Practical experience building MCP servers or CLI tooling for agents. - Exposure to agent orchestration frameworks (Claude Agent SDK, LangGraph, or similar) or LLM evaluation / observability tooling. - Hands-on experience with image / video generation models. - Startup experience, or having built something 0 to 1 (side projects count). 💰 Compensation - $300k+ total compensation including base salary, bonus, equity. - Equity — meaningful ownership in what you build. - High autonomy, high growth environment. 🌍 Work Setup - Bay Area preferred (hybrid available). - Visa sponsorship available. 🎯 What Success Looks Like in 3–6 Months - You've shipped a production agent workflow inside OpenArt's ecosystem, and real users are running it. - You've meaningfully improved a piece of the harness, a set of Agent Skills, or the eval loop — and can point to the metric that moved. - You own at least one part of the agent stack end to end and are the go-to person for it. - You've formed a tight feedback loop with design, founders, and real users. - You're proposing improvements and catching problems early, not just executing on specs. If you want to build the agents that turn frontier models into creative tools millions of people use — and grow fast while doing it — we'd love to talk.69:T1d96,

🎨 About OpenArt

OpenArt is an AI storytelling and visual creation platform used by millions of creators worldwide. We're building the next generation of creative tools powered by cutting-edge AI — enabling anyone to create videos, visuals, characters, and stories with unprecedented speed and imagination.

We believe the future of creativity is AI-native, and we're shaping that future.

🚀 Why Join OpenArt

- Ground floor of a new category — help define what "agent" means for creative work, the way Director redefined what "directing" means for video.

- Small team, massive surface area — engineers own real systems end to end, not slices.

- Real scale, real stakes — your agents run for millions of creators, and what you ship shows up in the product within days.

- Founder-led engineering culture — both founders are technical and deeply involved in product and architecture.

- AI-native product — you'll help decide how frontier models get orchestrated and exposed as experiences that feel effortless rather than technical.

- High ownership, low process — we value judgment, clarity, and speed over bureaucracy.

🎯 About the Role

We're hiring engineers to build the agent layer behind OpenArt's next generation of creative products, starting with Director — which lets anyone "vibe direct" a multi-minute cinematic video by chatting with AI. You'll be building the agent infrastructure that pushes experiences like this further: longer-horizon planning, tool use across multiple model vendors, memory across sessions, and the harness that keeps all of it reliable in production.

This isn't a research role — it's a product-and-systems role. You'll build on and extend the agent harness (tool use, context management, multi-step planning, evaluation), develop the MCP servers and CLI tooling around it, and ship the frontend experiences that make all of it feel effortless rather than technical. You'll work closely with the founders, the agent team, and design to turn "what should this agent be able to do" into a system that actually does it — reliably, at scale.

🛠 What You'll Do

- Build and extend the agent harness that powers OpenArt's conversational and autonomous creative workflows: tool use, context/memory management, multi-step planning, and sub-agent orchestration.

- Build and maintain MCP servers and CLI-based agent tooling that connect our agents to internal services, external model vendors, and creative asset pipelines.

- Author reusable Agent Skills and structured workflows that let agents reliably handle complex, multi-step creative tasks — scene planning, storyboarding, asset generation, revision loops.

- Contribute to evaluation frameworks and observability so the team knows when an agent is working well, and can catch regressions before users do.

- Ship polished, production frontend experiences (React/Next.js) for agent-driven products, end to end.

- Make pragmatic trade-offs between speed, correctness, and scalability — and communicate them clearly.

- Partner closely with product, design, and GTM to translate user needs into technical solutions.

- Track the fast-moving agent ecosystem (MCP, Agent Skills, orchestration frameworks) and bring the best of it into OpenArt's stack.

⚙ What We're Looking For

Core Requirements

- 2+ years of full-stack engineering experience shipping production software.

- Hands-on experience building LLM-powered agents or applications that have reached real users — not just prototypes or demos.

- Working familiarity with agent harness concepts: tool use, context/memory management, multi-step planning, or sub-agent orchestration.

- Solid data modeling and schema design skills for real product use cases.

- A strong AI-assisted coding practice — you use tools like Claude Code, Codex, or Cursor as a core part of how you build, and have real opinions about what makes them work well.

- Product sense — you think about what makes an agent experience feel trustworthy and delightful, not just functional.

- Clear communication — you can explain agent behavior and technical trade-offs to teammates who aren't engineers.

Nice to Have

- Experience authoring reusable Agent Skills or structured agent workflows.

- Practical experience building MCP servers or CLI tooling for agents.

- Exposure to agent orchestration frameworks (Claude Agent SDK, LangGraph, or similar) or LLM evaluation / observability tooling.

- Hands-on experience with image / video generation models.

- Startup experience, or having built something 0 to 1 (side projects count).

💰 Compensation

- $300k+ total compensation including base salary, bonus, equity.

- Equity — meaningful ownership in what you build.

- High autonomy, high growth environment.

🌍 Work Setup

- Bay Area preferred (hybrid available).

- Visa sponsorship available.

🎯 What Success Looks Like in 3–6 Months

- You've shipped a production agent workflow inside OpenArt's ecosystem, and real users are running it.

- You've meaningfully improved a piece of the harness, a set of Agent Skills, or the eval loop — and can point to the metric that moved.

- You own at least one part of the agent stack end to end and are the go-to person for it.

- You've formed a tight feedback loop with design, founders, and real users.

- You're proposing improvements and catching problems early, not just executing on specs.

If you want to build the agents that turn frontier models into creative tools millions of people use — and grow fast while doing it — we'd love to talk.

6a:T1b52,🎨 About OpenArt OpenArt is an AI storytelling and visual creation platform used by millions of creators worldwide. We're building the next generation of creative tools powered by cutting-edge AI — enabling anyone to create videos, visuals, characters, and stories with unprecedented speed and imagination. We believe the future of creativity is AI-native, and we're shaping that future. 🚀 Why Join OpenArt - Ground floor of a new category — help define what "agent" means for creative work, the way Director redefined what "directing" means for video. You'll set the architecture everything else is built on. - Own the whole agent layer — not a service, not a slice. Harness, orchestration, memory, evals, tooling: the entire system is yours to design and lead. - Real scale, real stakes — your agents run for millions of creators, and every improvement in reliability, latency, or quality shows up in the product within days. - Founder-led engineering culture — both founders are technical and deeply involved in product and architecture. - AI-native product — you'll decide how frontier models get orchestrated, constrained, and exposed as experiences that feel effortless rather than technical. - High ownership, low process — we value judgment, clarity, and speed over bureaucracy. 🎯 About the Role We're hiring a senior or staff engineer to lead the agentic architecture behind OpenArt's next generation of creative products, starting with Director — which lets anyone "vibe direct" a multi-minute cinematic video by chatting with AI. You'll own the agent infrastructure that pushes experiences like this further: longer-horizon planning, tool use across multiple model vendors, memory across sessions, sub-agent orchestration, and the harness that keeps all of it reliable in production. This isn't a research role — it's a product-and-systems leadership role. You'll define the architecture of the agent harness (tool use, context engineering, multi-step planning, orchestration, evaluation), set the engineering standards the team builds against, and stay hands-on: building the MCP servers and CLI tooling around it, and shipping the frontend experiences on top of it. You'll work directly with the founders and design to turn "what should this agent be able to do" into a system that actually does it — reliably, at scale — and you'll be the technical voice that decides how. 🛠 What You'll Do - Own the end-to-end architecture of the agent harness that powers OpenArt's conversational and autonomous creative workflows: tool use, context/memory management, multi-step planning, and sub-agent orchestration. - Design how agents plan and execute long-running creative tasks — scene planning, storyboarding, asset generation, revision loops — across multiple model vendors, with clear failure modes and recovery paths. - Build and maintain MCP servers and CLI-based agent tooling that connect our agents to internal services, external model vendors, and creative asset pipelines. - Author reusable Agent Skills and structured workflows, and define the patterns other engineers use to add new ones. - Build the evaluation and observability stack — automated evals, regression suites, tracing, and quality gates — so the team knows when an agent is working well and catches regressions before users do. - Drive reliability, latency, and cost per successful run as first-class metrics, not afterthoughts. - Ship polished, production frontend experiences (React/Next.js) for agent-driven products, end to end. - Set the technical bar for agent engineering at OpenArt: architecture reviews, engineering standards, and mentorship as the team grows around you. - Track the fast-moving agent ecosystem (MCP, Agent Skills, orchestration frameworks, new model capabilities) and decide what belongs in OpenArt's stack — and what doesn't. - Partner closely with founders, product, design, and GTM to translate user needs into technical direction, and communicate trade-offs clearly. ⚙ What We're Looking For Core Requirements - 7+ years of full-stack engineering experience shipping and owning production systems at scale, including 2+ years building LLM-powered agents in production — not just prototypes or demos. - Deep, hands-on experience with agent architecture: tool use, context/memory management, multi-step planning, sub-agent orchestration, and long-running workflows. - Track record of designing evaluation frameworks and observability for agentic systems — you know how to measure whether an agent is working and how to keep it from regressing. - Strong system design and data modeling skills — comfortable reasoning about state, schemas, and APIs for real product use cases. - Experience authoring reusable Agent Skills, structured agent workflows, or equivalent abstractions that other engineers build on. - Demonstrated technical leadership — you've set direction for a system or team, and people follow your architectural calls because they've been right before. - A strong AI-assisted coding practice — you use tools like Claude Code, Codex, or Cursor as a core part of how you build, and have real opinions about what makes them work well. - High product sense — you think about what makes an agent experience feel trustworthy and delightful, not just functional. - Strong communication skills — you can explain agent behavior and technical trade-offs clearly to founders, designers, and non-technical teammates. Nice to Have - Practical experience building MCP servers and CLI tooling for agents. - Experience with agent orchestration frameworks (Claude Agent SDK, LangGraph, or similar) and LLM evaluation / observability tooling. - Hands-on experience with image / video generation models and creative asset pipelines. - Experience with guardrails, safety, and cost controls for autonomous systems. - Startup experience or ownership of a product surface from 0 to 1. 💰 Compensation - $500k+ total compensation including base salary, bonus, equity. - Equity — meaningful ownership in what you build. - High autonomy, high growth environment. 🌍 Work Setup - Bay Area preferred (hybrid available). - Visa sponsorship available. 🎯 What Success Looks Like in 3–6 Months - The agent harness has a clear, documented architecture that the team builds against — and it's yours. - A production agent workflow you designed is shipped and running for real users inside OpenArt's ecosystem. - An eval and observability loop exists, and regressions get caught before users see them. - Reliability, latency, or cost per successful run has measurably improved on a workflow that matters. - You've formed a tight feedback loop with design, founders, and real users. - You're setting the direction for agent engineering — pushing ideas, not waiting for specs. If you want to lead the architecture that turns frontier models into creative agents millions of people use — and own it from day one — we'd love to talk.6b:T23e5,

🎨 About OpenArt

We believe the future of creativity is AI-native, and we're shaping that future.

🚀 Why Join OpenArt

- Ground floor of a new category — help define what "agent" means for creative work, the way Director redefined what "directing" means for video. You'll set the architecture everything else is built on.

- Own the whole agent layer — not a service, not a slice. Harness, orchestration, memory, evals, tooling: the entire system is yours to design and lead.

- Real scale, real stakes — your agents run for millions of creators, and every improvement in reliability, latency, or quality shows up in the product within days.

- Founder-led engineering culture — both founders are technical and deeply involved in product and architecture.

- AI-native product — you'll decide how frontier models get orchestrated, constrained, and exposed as experiences that feel effortless rather than technical.

- High ownership, low process — we value judgment, clarity, and speed over bureaucracy.

🎯 About the Role

We're hiring a senior or staff engineer to lead the agentic architecture behind OpenArt's next generation of creative products, starting with Director — which lets anyone "vibe direct" a multi-minute cinematic video by chatting with AI. You'll own the agent infrastructure that pushes experiences like this further: longer-horizon planning, tool use across multiple model vendors, memory across sessions, sub-agent orchestration, and the harness that keeps all of it reliable in production.

This isn't a research role — it's a product-and-systems leadership role. You'll define the architecture of the agent harness (tool use, context engineering, multi-step planning, orchestration, evaluation), set the engineering standards the team builds against, and stay hands-on: building the MCP servers and CLI tooling around it, and shipping the frontend experiences on top of it. You'll work directly with the founders and design to turn "what should this agent be able to do" into a system that actually does it — reliably, at scale — and you'll be the technical voice that decides how.

🛠 What You'll Do

- Own the end-to-end architecture of the agent harness that powers OpenArt's conversational and autonomous creative workflows: tool use, context/memory management, multi-step planning, and sub-agent orchestration.

- Design how agents plan and execute long-running creative tasks — scene planning, storyboarding, asset generation, revision loops — across multiple model vendors, with clear failure modes and recovery paths.

- Build and maintain MCP servers and CLI-based agent tooling that connect our agents to internal services, external model vendors, and creative asset pipelines.

- Author reusable Agent Skills and structured workflows, and define the patterns other engineers use to add new ones.

- Build the evaluation and observability stack — automated evals, regression suites, tracing, and quality gates — so the team knows when an agent is working well and catches regressions before users do.

- Drive reliability, latency, and cost per successful run as first-class metrics, not afterthoughts.

- Ship polished, production frontend experiences (React/Next.js) for agent-driven products, end to end.

- Set the technical bar for agent engineering at OpenArt: architecture reviews, engineering standards, and mentorship as the team grows around you.

- Track the fast-moving agent ecosystem (MCP, Agent Skills, orchestration frameworks, new model capabilities) and decide what belongs in OpenArt's stack — and what doesn't.

- Partner closely with founders, product, design, and GTM to translate user needs into technical direction, and communicate trade-offs clearly.

⚙ What We're Looking For

Core Requirements

- 7+ years of full-stack engineering experience shipping and owning production systems at scale, including 2+ years building LLM-powered agents in production — not just prototypes or demos.

- Deep, hands-on experience with agent architecture: tool use, context/memory management, multi-step planning, sub-agent orchestration, and long-running workflows.

- Track record of designing evaluation frameworks and observability for agentic systems — you know how to measure whether an agent is working and how to keep it from regressing.

- Strong system design and data modeling skills — comfortable reasoning about state, schemas, and APIs for real product use cases.

- Experience authoring reusable Agent Skills, structured agent workflows, or equivalent abstractions that other engineers build on.

- Demonstrated technical leadership — you've set direction for a system or team, and people follow your architectural calls because they've been right before.

- A strong AI-assisted coding practice — you use tools like Claude Code, Codex, or Cursor as a core part of how you build, and have real opinions about what makes them work well.

- High product sense — you think about what makes an agent experience feel trustworthy and delightful, not just functional.

- Strong communication skills — you can explain agent behavior and technical trade-offs clearly to founders, designers, and non-technical teammates.

Nice to Have

- Practical experience building MCP servers and CLI tooling for agents.

- Experience with agent orchestration frameworks (Claude Agent SDK, LangGraph, or similar) and LLM evaluation / observability tooling.

- Hands-on experience with image / video generation models and creative asset pipelines.

- Experience with guardrails, safety, and cost controls for autonomous systems.

- Startup experience or ownership of a product surface from 0 to 1.

💰 Compensation

- $500k+ total compensation including base salary, bonus, equity.

- Equity — meaningful ownership in what you build.

- High autonomy, high growth environment.

🌍 Work Setup

- Bay Area preferred (hybrid available).

- Visa sponsorship available.

🎯 What Success Looks Like in 3–6 Months

- The agent harness has a clear, documented architecture that the team builds against — and it's yours.

- A production agent workflow you designed is shipped and running for real users inside OpenArt's ecosystem.

- An eval and observability loop exists, and regressions get caught before users see them.

- Reliability, latency, or cost per successful run has measurably improved on a workflow that matters.

- You've formed a tight feedback loop with design, founders, and real users.

- You're setting the direction for agent engineering — pushing ideas, not waiting for specs.

If you want to lead the architecture that turns frontier models into creative agents millions of people use — and own it from day one — we'd love to talk.

6c:T259c,About us Coalition is the world's first Active Insurance provider designed to help prevent digital risk before it strikes. Founded in 2017, Coalition combines comprehensive insurance coverage and innovative cybersecurity tools to help businesses manage and mitigate potential cyberattacks. Opportunities to make an impact with bold thinking are real—and happening daily at Coalition. About the role We are hiring a Software Engineer for the Automation team to use AI to solve and automate manual work across Coalition. This is a hands-on AI engineering role: authoring skills, wiring agents to our systems of record through MCP, building evals, and deploying agents that non-engineers depend on every day. You will not be starting from zero. We have early workflows live in production, more proofs of concept in the oven, and working hypotheses about how all of this should fit together. We want someone who will pressure test those hypotheses against their own experience and change our minds where we have it wrong. Deep, current, practical experience building with LLMs is the single most important qualification for this job. The other half of the role is proximity to the business. Think of this as a Forward Deployed Engineer, pointed inward: your customers are other Coalition teams. You will work directly with the teams whose work we are automating, learn how they actually operate, and turn that into a playbook. Getting from that conversation to something an agent can run reliably is the core of the work. You will also work day to day with our data team to get at the data these agents depend on. This is an individual contributor role with no direct reports. Responsibilities Author skills: Turn a team's process, judgment, and edge cases into reusable skills that agents can invoke. Write them so the next project can reuse them instead of rebuilding them. Build within our agent architecture: Wire agents to the systems of record through MCP servers, compose skills and tools into working workflows, and add new MCP surface area when a system we need is not covered yet. Build evals: Define what "correct" means with the business team before you build. Build the eval set, establish a baseline, and use it to make calls about what ships. Nothing goes live on vibes. Deploy and support agents: Take agents from prototype to production, including permissions, human-in-the-loop checkpoints, audit trails, and rollback. Tune from how they behave once real users are in front of them, and leave behind docs and an owner on the business team who can run it without you. Partner with the data team: Work with our data engineers on the access, models, and quality of the data your agents read and write. Bring them requirements early rather than working around them. Skills and Qualifications AI application building (primary): Substantial hands-on experience shipping LLM-backed applications: prompting, tool use, agentic loops, and multi-step workflows. You know where these break and how to contain the damage when they do. You have shipped at least one agent that non-engineers depend on. Agent tooling: Practical experience with MCP, skill or tool authoring, and orchestrating agents against real systems of record. Evals: You have built eval sets and used them to make decisions, not just to make a slide look good. Coding agents: Fluent with agentic coding tools like Cursor. You use them to move faster inside codebases you did not write. Core development: Strong Python. Comfortable with SQL and working directly against production data models. Business fluency: You can sit with a non-technical operator, map their process, and translate it into requirements without a PM in the room. Comfort with ambiguity: Undocumented processes, half-broken internal tools, and unclear ownership are the normal starting conditions. You find the thread and pull it. Shipping discipline: Git, CI, containers, and enough operational instinct to deploy your own work and keep it healthy. Experience: At least 5+ years of hands-on software development experience. It's not the years, it's the miles. Bonus Points Experience building or maintaining MCP servers. Prior solutions engineering, professional services, internal tools, or startup founding experience. Working knowledge of the systems we automate against: Linear, Jira, Zendesk, Salesforce, Snowflake. Insurance, fintech, or another regulated domain where being wrong has a compliance cost. Compensation As a remote-first organization, our compensation reflects the cost of labor across several Canadian geographic markets. In Alberta, British Columbia & Ontario the base salary for this position ranges from $128,500/year up to $160,600/year. For all other locations, the base salary for this position ranges from $115,600/year up to $144,540/year. Consistent with applicable laws, an employee's pay within this range is based on a number of factors, which include but are not limited to relevant education, skills, job-related knowledge, qualifications, work experience, credentials, and/or geographic location. Your Recruiter can share more on the specific target salary range for your location during the interview process. Coalition, Inc. reserves the right to modify this range as needed. Vacancy Status: This posting is for an existing vacancy. Use of AI: We utilize AI-assisted tools to help us organize and review the high volume of applications we receive. However, our human recruiting team makes all final hiring decisions and interview selections, valuing personal connection over algorithms. Application Updates: Consistent with our commitment to transparency, if you are interviewed for this role, we will provide a status update on your application within 45 days of your final interview. Perks 100% medical, dental, and vision coverage Flexible PTO Annual home office stipend and WeWork access Mental & physical health wellness programs like Headspace, Lumino, and more! Competitive compensation and opportunity for advancement Why Coalition? Work at Coalition is centered on the joint mission to Protect the Unprotected. We have built a remote-first, highly inclusive culture that welcomes people from diverse backgrounds. We trust each other to take responsibility, share ownership of outcomes, and put in the work together to protect businesses from digital risk. Coalition’s exceptional growth stems from its ability to address real-world problems for organizations of all sizes while remaining true to our founding values of character, humility, responsibility, purpose, authenticity, and inclusion. We’re always looking for collaborative, inquisitive individuals to join #OurCoalition. Visit our Newsroom > Privacy Notice Coalition is committed to protecting your privacy and handling your personal information responsibly. We collect, use, and store personal information as necessary for the recruitment process and in compliance with applicable privacy laws and regulations in all regions where we operate. We want you to understand what personal information we collect, how we use it, and your rights regarding access, correction, and deletion of your data where applicable. Information submitted, collected, and processed as part of your application is subject to Coalition's Privacy Policy. For further details, please review our full Privacy Policy or contact us with any questions regarding how your information is handled. Our Privacy Policy > Safe Hiring Notice All legitimate communication from Coalition comes from @coalitioninc.com emails, and open roles are listed only on our Careers page . We never ask for payment, banking details, or personal identification before an offer is accepted through our secure systems. If you believe you’ve been a victim of fraudulent recruiting, follow guidance from the Federal Trade Commission (FTC). Anti-Discrimination Notice Coalition is proud to be an Equal Opportunity employer. Our policy is to provide equal employment opportunities to all individuals, without discrimination or harassment on the basis of any characteristic protected by applicable laws in each country where we operate. This commitment includes, but is not limited to, ensuring equal treatment in recruitment, selection, training, promotion, transfer, compensation, and all other aspects of employment. Coalition does not tolerate discrimination or harassment of any kind, and we are dedicated to fostering an inclusive and supportive workplace. Accommodations Coalition is committed to providing reasonable accommodations to qualified individuals with disabilities, including applicants and employees, in accordance with applicable laws and regulations in each country where we operate. Our policy is to support equal opportunity in the hiring process by considering qualified applicants regardless of disability or other protected characteristics, unless providing accommodation would impose an undue hardship or disproportionate burden. If you require accommodation to complete an application, interview, pre-employment testing, or participate in the selection process, please contact us at candidateaccommodations@coalitioninc.com . We also consider all qualified applicants, including those with criminal histories, in line with applicable laws and regulations in each jurisdiction. To all recruitment agencies: Coalition does not accept unsolicited agency resumes. Do not forward resumes to our email alias, employees, or other physical or virtual organization locations. Coalition is not responsible for any fees related to unsolicited resumes.6d:T2c3e,

About us

Coalition is the world's first Active Insurance provider designed to help prevent digital risk before it strikes. Founded in 2017, Coalition combines comprehensive insurance coverage and innovative cybersecurity tools to help businesses manage and mitigate potential cyberattacks.

Opportunities to make an impact with bold thinking are real—and happening daily at Coalition.

About the role

We are hiring a Software Engineer for the Automation team to use AI to solve and automate manual work across Coalition. This is a hands-on AI engineering role: authoring skills, wiring agents to our systems of record through MCP, building evals, and deploying agents that non-engineers depend on every day. You will not be starting from zero. We have early workflows live in production, more proofs of concept in the oven, and working hypotheses about how all of this should fit together. We want someone who will pressure test those hypotheses against their own experience and change our minds where we have it wrong. Deep, current, practical experience building with LLMs is the single most important qualification for this job.

The other half of the role is proximity to the business. Think of this as a Forward Deployed Engineer, pointed inward: your customers are other Coalition teams. You will work directly with the teams whose work we are automating, learn how they actually operate, and turn that into a playbook. Getting from that conversation to something an agent can run reliably is the core of the work. You will also work day to day with our data team to get at the data these agents depend on. This is an individual contributor role with no direct reports.

Responsibilities

Author skills: Turn a team's process, judgment, and edge cases into reusable skills that agents can invoke. Write them so the next project can reuse them instead of rebuilding them.
Build within our agent architecture: Wire agents to the systems of record through MCP servers, compose skills and tools into working workflows, and add new MCP surface area when a system we need is not covered yet.
Build evals: Define what "correct" means with the business team before you build. Build the eval set, establish a baseline, and use it to make calls about what ships. Nothing goes live on vibes.
Deploy and support agents: Take agents from prototype to production, including permissions, human-in-the-loop checkpoints, audit trails, and rollback. Tune from how they behave once real users are in front of them, and leave behind docs and an owner on the business team who can run it without you.
Partner with the data team: Work with our data engineers on the access, models, and quality of the data your agents read and write. Bring them requirements early rather than working around them.

Skills and Qualifications

AI application building (primary): Substantial hands-on experience shipping LLM-backed applications: prompting, tool use, agentic loops, and multi-step workflows. You know where these break and how to contain the damage when they do. You have shipped at least one agent that non-engineers depend on.
Agent tooling: Practical experience with MCP, skill or tool authoring, and orchestrating agents against real systems of record.
Evals: You have built eval sets and used them to make decisions, not just to make a slide look good.
Coding agents: Fluent with agentic coding tools like Cursor. You use them to move faster inside codebases you did not write.
Core development: Strong Python. Comfortable with SQL and working directly against production data models.
Business fluency: You can sit with a non-technical operator, map their process, and translate it into requirements without a PM in the room.
Comfort with ambiguity: Undocumented processes, half-broken internal tools, and unclear ownership are the normal starting conditions. You find the thread and pull it.
Shipping discipline: Git, CI, containers, and enough operational instinct to deploy your own work and keep it healthy.
Experience: At least 5+ years of hands-on software development experience. It's not the years, it's the miles.

Bonus Points

Experience building or maintaining MCP servers.
Prior solutions engineering, professional services, internal tools, or startup founding experience.
Working knowledge of the systems we automate against: Linear, Jira, Zendesk, Salesforce, Snowflake.
Insurance, fintech, or another regulated domain where being wrong has a compliance cost.

Compensation

As a remote-first organization, our compensation reflects the cost of labor across several Canadian geographic markets.

In Alberta, British Columbia & Ontario the base salary for this position ranges from $128,500/year up to $160,600/year.

For all other locations, the base salary for this position ranges from $115,600/year up to $144,540/year.

Consistent with applicable laws, an employee's pay within this range is based on a number of factors, which include but are not limited to relevant education, skills, job-related knowledge, qualifications, work experience, credentials, and/or geographic location. Your Recruiter can share more on the specific target salary range for your location during the interview process. Coalition, Inc. reserves the right to modify this range as needed.

Vacancy Status: This posting is for an existing vacancy.

Use of AI: We utilize AI-assisted tools to help us organize and review the high volume of applications we receive. However, our human recruiting team makes all final hiring decisions and interview selections, valuing personal connection over algorithms.

Application Updates: Consistent with our commitment to transparency, if you are interviewed for this role, we will provide a status update on your application within 45 days of your final interview.

Perks

100% medical, dental, and vision coverage
Flexible PTO
Annual home office stipend and WeWork access
Mental & physical health wellness programs like Headspace, Lumino, and more!
Competitive compensation and opportunity for advancement

Why Coalition?

Work at Coalition is centered on the joint mission to Protect the Unprotected. We have built a remote-first, highly inclusive culture that welcomes people from diverse backgrounds. We trust each other to take responsibility, share ownership of outcomes, and put in the work together to protect businesses from digital risk. Coalition’s exceptional growth stems from its ability to address real-world problems for organizations of all sizes while remaining true to our founding values of character, humility, responsibility, purpose, authenticity, and inclusion.

We’re always looking for collaborative, inquisitive individuals to join #OurCoalition.

Visit our Newsroom >

Privacy Notice

Coalition is committed to protecting your privacy and handling your personal information responsibly. We collect, use, and store personal information as necessary for the recruitment process and in compliance with applicable privacy laws and regulations in all regions where we operate. We want you to understand what personal information we collect, how we use it, and your rights regarding access, correction, and deletion of your data where applicable. Information submitted, collected, and processed as part of your application is subject to Coalition's Privacy Policy. For further details, please review our full Privacy Policy or contact us with any questions regarding how your information is handled.

Our Privacy Policy >

Safe Hiring Notice

All legitimate communication from Coalition comes from @coalitioninc.com emails, and open roles are listed only on our Careers page. We never ask for payment, banking details, or personal identification before an offer is accepted through our secure systems. If you believe you’ve been a victim of fraudulent recruiting, follow guidance from the Federal Trade Commission (FTC).

Anti-Discrimination Notice

Coalition is proud to be an Equal Opportunity employer. Our policy is to provide equal employment opportunities to all individuals, without discrimination or harassment on the basis of any characteristic protected by applicable laws in each country where we operate. This commitment includes, but is not limited to, ensuring equal treatment in recruitment, selection, training, promotion, transfer, compensation, and all other aspects of employment. Coalition does not tolerate discrimination or harassment of any kind, and we are dedicated to fostering an inclusive and supportive workplace.

Accommodations

Coalition is committed to providing reasonable accommodations to qualified individuals with disabilities, including applicants and employees, in accordance with applicable laws and regulations in each country where we operate. Our policy is to support equal opportunity in the hiring process by considering qualified applicants regardless of disability or other protected characteristics, unless providing accommodation would impose an undue hardship or disproportionate burden. If you require accommodation to complete an application, interview, pre-employment testing, or participate in the selection process, please contact us at candidateaccommodations@coalitioninc.com. We also consider all qualified applicants, including those with criminal histories, in line with applicable laws and regulations in each jurisdiction.

To all recruitment agencies: Coalition does not accept unsolicited agency resumes. Do not forward resumes to our email alias, employees, or other physical or virtual organization locations. Coalition is not responsible for any fees related to unsolicited resumes.

6e:Tdc9,Why RoboForce RoboForce is an AI robotics company developing Physical AI–powered Robo-Labor for dull, dirty, and dangerous work. The company's robots are engineered for demanding industrial environments, with a focus on real-world deployment and scalability. We are hiring a Lead AI Native Engineer to build RoboForce's vertical agents and shared agent foundation. Reporting to the co-founder, you will turn frontier models into systems for strategy and engineering. This is a hands-on technical leadership role: you will write code, set architecture, and enable agent development—not lead People programs or organizational transformation. Responsibilities - Build vertical agents. Own end-to-end agents for high-value workflows across research, software, hardware, data, and operations—from problem definition through production use. - Establish the agent foundation. Build reusable primitives for models, tools, orchestration, context, memory, retrieval, permissions, human approval, and long-running execution. - Create the context layer. Connect agents to trusted data through APIs, pipelines, MCP servers, and integrations with clear provenance and access control. - Own evaluation and reliability. Build benchmarks, regression tests, tracing, monitoring, and failure-analysis loops across quality, latency, cost, security, and resilience. - Advance frontier agent usage. Evaluate new models, coding agents, SDKs, and patterns, then turn useful capabilities into maintainable systems rather than demos. - Support strategic initiatives. Help company leadership apply agents and analytical systems to market and customer intelligence, partnerships, fundraising, diligence, scenario analysis, and executive decisions. - Provide technical leadership. Set architecture and engineering standards, review designs and code, and create reusable patterns for the technical team. Requirements - 5+ years in software engineering, applied AI, ML systems, or a related field, with strong zero-to-one technical judgment. - Experience at a frontier AI lab, leading AI company, or comparable team working at the edge of current model capabilities. - A track record shipping production agentic systems that real users depend on—not only prompts, prototypes, or demos. - Deep experience with frontier model APIs, tool use, orchestration, context engineering, retrieval, memory, and multi-step workflows. - Experience building evaluations, regression tests, observability, and production failure-analysis loops for AI systems. - Exceptional fluency with AI-native development workflows using Claude Code, Codex, Cursor, agent SDKs, or equivalent systems, with a rigorous understanding of where agents work and fail. - Strong product judgment: able to turn an ambiguous decision or workflow into a useful, secure, measurable system. - Requires 5 days/week in-office collaboration with the team. Bonus Qualifications - Experience with post-training, model evaluation, inference, or research infrastructure at a frontier lab or model company. - Experience with MCP infrastructure, developer platforms, knowledge graphs, RAG, or secure enterprise integrations. - Background in robotics, autonomous systems, industrial automation, or another technically complex physical-world domain. Benefits - Competitive stock options/equity programs. - Health, dental, and vision insurance, 401(k) plan. - Visa sponsorship and green card support for qualified candidates. - Lunches and dinners, a fully stocked kitchen, and regular team-building events.6f:T1222,

Why RoboForce

RoboForce is an AI robotics company developing Physical AI–powered Robo-Labor for dull, dirty, and dangerous work. The company's robots are engineered for demanding industrial environments, with a focus on real-world deployment and scalability.

We are hiring a Lead AI Native Engineer to build RoboForce's vertical agents and shared agent foundation. Reporting to the co-founder, you will turn frontier models into systems for strategy and engineering. This is a hands-on technical leadership role: you will write code, set architecture, and enable agent development—not lead People programs or organizational transformation.

Responsibilities

- Build vertical agents. Own end-to-end agents for high-value workflows across research, software, hardware, data, and operations—from problem definition through production use.

- Establish the agent foundation. Build reusable primitives for models, tools, orchestration, context, memory, retrieval, permissions, human approval, and long-running execution.

- Create the context layer. Connect agents to trusted data through APIs, pipelines, MCP servers, and integrations with clear provenance and access control.

- Own evaluation and reliability. Build benchmarks, regression tests, tracing, monitoring, and failure-analysis loops across quality, latency, cost, security, and resilience.

- Advance frontier agent usage. Evaluate new models, coding agents, SDKs, and patterns, then turn useful capabilities into maintainable systems rather than demos.

- Support strategic initiatives. Help company leadership apply agents and analytical systems to market and customer intelligence, partnerships, fundraising, diligence, scenario analysis, and executive decisions.

- Provide technical leadership. Set architecture and engineering standards, review designs and code, and create reusable patterns for the technical team.

Requirements

- 5+ years in software engineering, applied AI, ML systems, or a related field, with strong zero-to-one technical judgment.

- Experience at a frontier AI lab, leading AI company, or comparable team working at the edge of current model capabilities.

- A track record shipping production agentic systems that real users depend on—not only prompts, prototypes, or demos.

- Deep experience with frontier model APIs, tool use, orchestration, context engineering, retrieval, memory, and multi-step workflows.

- Experience building evaluations, regression tests, observability, and production failure-analysis loops for AI systems.

- Exceptional fluency with AI-native development workflows using Claude Code, Codex, Cursor, agent SDKs, or equivalent systems, with a rigorous understanding of where agents work and fail.

- Strong product judgment: able to turn an ambiguous decision or workflow into a useful, secure, measurable system.

- Requires 5 days/week in-office collaboration with the team.

Bonus Qualifications

- Experience with post-training, model evaluation, inference, or research infrastructure at a frontier lab or model company.

- Experience with MCP infrastructure, developer platforms, knowledge graphs, RAG, or secure enterprise integrations.

- Background in robotics, autonomous systems, industrial automation, or another technically complex physical-world domain.

Benefits

- Competitive stock options/equity programs.

- Health, dental, and vision insurance, 401(k) plan.

- Visa sponsorship and green card support for qualified candidates.

- Lunches and dinners, a fully stocked kitchen, and regular team-building events.

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