Member of Technical Staff - Senior Software Engineer

About Patronus AI

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 likeFinanceBench,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

As a Senior Software Engineer at Patronus AI, you'll build high-quality simulations used to train, evaluate, and improve AI agents - and the platform tooling that powers them. These environments model real-world workflows and generate the interaction data needed to improve frontier models.

This is a highly autonomous role: you'll own complex agent environments end-to-end, from frontend to backend, and work closely with Applied Researchers to turn environment designs, task specs, and verifier requirements into robust software.

Your work will help frontier labs stress-test and improve the next generation of AI agents, advancing the path toward safe, human-aligned general intelligence.

In this role, you will:

• Build RL environments end-to-end - frontend interfaces, backend services, APIs, data models, and workflows - using technologies like React/TypeScript, Next.js, Python/FastAPI, and Postgres.
• Think critically about task coverage, environment correctness, edge cases, and adversarial agent behavior.
• Work closely with Applied Researchers, subject-matter experts, and QA specialists to ensure the interaction data your environments produce is high quality, and continually find ways to make it better.
• Build tooling and shared services - like agentic systems and frontends for trajectory review, verifier debugging, reward hacking checks, and environment QA - that make every team faster, more consistent, and more confident in the environments they ship.
• Develop reusable patterns and libraries for building simulated worlds across different domains.
• Build the shared infrastructure that lets us run environments across heterogeneous targets (Linux, macOS, mobile, browser-based) and benchmark them against frontier external models and our own for pre- and post-training evaluation.
• Be a power user of AI coding tools (Claude Code, Codex, and similar) - curious about how they work under the hood, with ideas for extending them and building new tooling on top.
• Operate as a senior IC: own architecture, make tradeoffs, drive execution, mentor others, and raise the engineering bar.

Qualifications

“The number one qualification to succeed in this machine learning course is gumption” - John Lafferty, CS Professor at Yale

Above all, we look for a proactive mindset, willingness to learn, relentless drive, and passion for engineering and product. You are a great fit if you have a background in the following:

• 4+ years as a software engineer, full stack engineer, or similar.
• A track record of owning complex systems end-to-end, from ambiguous requirements through production launch - ideally at a startup or on a small, high-velocity team.
• Strong experience building production software with React, TypeScript, and modern frontend patterns.
• Strong experience designing APIs, services, data models, and workflows - ideally with TypeScript, Go, Next.js, Python, FastAPI, and Postgres.
• A solid understanding of how modern LLMs work at the API level - tool calling, agent loops, context management - and experience building custom tools, harnesses, or non-trivial agent integrations.
• Strong intuition for edge cases, correctness, failure modes, and adversarial behavior in agentic systems.
• Familiarity with Kubernetes, distributed systems, sandboxing, orchestration, or platform engineering.
• Daily user of agentic coding tools (Claude Code, Codex, Cursor, or similar) with instincts for where to build automations, extensions, or entirely new tools on top.

Nice to have:

• Experience with reinforcement learning, agent evaluation, verifiers, reward models, or ML infrastructure - though deep applied research experience isn't required.
• Experience with Playwright, Selenium, or browser automation.
• BS, MS, or PhD in Computer Science, Machine Learning, Statistics, Mathematics, or a related quantitative field - or equivalent experience.

To support close collaboration, this role is based in our San Francisco headquarters and requires in-office attendance 5 days a week.

The expected base salary range for this role is $150,000 - $300,000 USD. In addition to base salary, we offer equity and benefits. Actual compensation will be determined based on experience, qualifications, skills, and location.

Benefits

• Competitive salary and equity packages
• 15 days of paid vacation per annum
• Parental & sick leave
• Health, dental, and vision insurance plans
• 401(k) plan + matching
• In-office lunch & dinner
• Sponsored personal tax accounting 
• Whoop band
• Monthly meal stipend
• Monthly health and wellness stipend
• Equinox membership
• Fun global offsites!

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.

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