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About the Role
Patronus AI is a frontier lab developing simulation research and infrastructure to accelerate progress toward human-aligned AGI. We are on a mission to simulate all of the world’s intelligence. We are the team behind some of the earliest and most influential research in AI evaluation like FinanceBench, Lynx, SimpleSafetyTests, CopyrightCatcher, Humanity’s Last Exam, and more. We are formerly AI researchers and engineers from companies like Meta AI, Amazon AGI, and Google. Our customers include foundation model labs and Fortune 500 enterprises like Adobe. We are backed by top-tier investors like Lightspeed Venture Partners, Notable Capital, Stanford University, Noam Brown, Gokul Rajaram, and more.
Responsibilities
- Build agent environments and simulations end-to-end, including frontend interfaces, backend services, APIs, data models, tools, and realistic workflows used to train and evaluate AI agents.
- Build the infrastructure that powers our agent gym, including orchestration, sandboxing, packaging, benchmarking, and systems for running environments across heterogeneous targets.
- Develop internal platforms and developer tools used by researchers and engineers, from backends and dashboards to CLIs, SDKs, review agents, codegen helpers, and workflow automations.
- Build and operate ML infrastructure, including model deployment and serving, evaluation systems, GPU workloads, and the services that make compute accessible to the broader team.
- Own systems from ambiguous idea through production. Define the problem, make architectural decisions, implement the solution, instrument it, and iterate based on how it performs in practice.
- Think deeply about correctness and failure modes. Design for edge cases, adversarial agent behavior, reproducibility, observability, and the messy realities of production systems.
- Partner closely with researchers to productionize experiments and build the software and infrastructure needed to turn research ideas into scalable systems.
- Be a power user of AI coding tools like Claude Code, Codex, Cursor, and similar tools — and build new tooling and automations on top of them when existing workflows aren't good enough.
- Move quickly without sacrificing judgment. Make pragmatic decisions about what needs to be robust today, what can evolve later, and where technical investment will create leverage for the team.
Requirements
- A track record of shipping non-trivial software end-to-end as an individual contributor, ideally at a startup or on a small, high-velocity team.
- Strong engineering fundamentals and meaningful depth in at least one of backend/infrastructure, frontend/product engineering, or ML systems, with the ability and desire to work across boundaries.
- Experience building production systems in languages such as Python, Go, and/or TypeScript, and the ability to become productive quickly in an unfamiliar stack.
- Experience working with modern LLMs and agents at the application level — including concepts like tool calling, agent loops, context management, harnesses, and evaluation.
- Strong engineering judgment around system design, correctness, reliability, failure modes, edge cases, and operational complexity.
- High independence. You can take an ambiguous goal, determine what needs to be built, find the people or information necessary to unblock yourself, and ship without requiring the work to be fully pre-scoped.
- Fluency with modern AI coding tools and a strong instinct for where AI can automate or accelerate engineering workflows.
- A BS, MS, or PhD in Computer Science, Machine Learning, Software Engineering, or a related quantitative field — or equivalent experience.
- Building complex full-stack products using technologies like React, TypeScript, Next.js, Python, relational databases, and modern API frameworks.
- Building developer platforms, distributed systems, orchestration systems, sandboxes, or internal infrastructure.
- Reinforcement learning environments, agent evaluation, verifiers, reward models, or benchmarking infrastructure.
- Deploying and serving ML models using managed inference providers or self-operated GPUs.
- GPU infrastructure, workload schedulers, Kubernetes, containers, CI/CD, and cloud infrastructure.
- MLOps/LLMOps tooling, experiment tracking, model registries, and production observability.
- Browser automation tools such as Playwright or Selenium.
- Building or deeply using complex enterprise software and understanding the operational edge cases that accumulate in real-world systems.
Skills
- Python
- Go
- TypeScript
- React
- Next.js
- Relational databases
- Modern API frameworks
- LLMs
- Agents
- Tool calling
- Agent loops
- Context management
- Harnesses
- Evaluation
- System design
- Correctness
- Reliability
- Failure modes
- Edge cases
- Operational complexity
- AI coding tools
- Distributed systems
- Orchestration systems
- Sandboxing
- Reinforcement learning
- MLOps
- LLMOps
- Experiment tracking
- Model registries
- Production observability
- Browser automation
- Playwright
- Selenium
- Enterprise software
Location
- San Francisco
Work Type
- Onsite
Experience Level
- Individual contributor
- Startup or small, high-velocity team experience
Education Level
- BS, MS, or PhD in Computer Science, Machine Learning, Software Engineering, or a related quantitative field, or equivalent experience
Salary/Compensations
- $175,000 - $300,000 USD
Benefits
- Equity
- Benefits
About the Company
- Patronus AI is a frontier lab developing simulation research and infrastructure to accelerate progress toward human-aligned AGI.
- We are on a mission to simulate all of the world’s intelligence.
- We are the team behind some of the earliest and most influential research in AI evaluation like FinanceBench, Lynx, SimpleSafetyTests, CopyrightCatcher, Humanity’s Last Exam, and more.
- We are formerly AI researchers and engineers from companies like Meta AI, Amazon AGI, and Google.
- Our customers include foundation model labs and Fortune 500 enterprises like Adobe.
- We are backed by top-tier investors like Lightspeed Venture Partners, Notable Capital, Stanford University, Noam Brown, Gokul Rajaram, and more.
Equal Opportunity
- Patronus AI is an equal opportunity employer. We celebrate diversity in our workplace, and all qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or other legally protected characteristics.