Member Of Technical Staff

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 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

As a Researcher at Patronus AI, you will own and drive foundational research that defines how agentic AI systems are trained, evaluated, and improved. You will work at the intersection of reinforcement learning, simulations, and scalable oversight, building systems that directly influence how frontier models are developed, stress-tested, and deployed.

This is a highly autonomous role. You will tackle open-ended research questions surrounding agent simulations and translate them into rigorous experiments, benchmarks, environments, and production systems. You will work across areas including reward design, tool simulations, agent cognition, behavior analysis, and scalable oversight, helping shape the industry standard for robust, high-quality environments.

Your work will inform how frontier labs design, train, evaluate, and improve the next generation of agents for complex, long-horizon tasks, advancing our path toward safe, human-aligned general intelligence.

In this role, you will: 

• Own ambitious research projects end-to-end, from identifying and formulating open-ended problems through experiment design, execution, analysis, and production impact.
• Advance research in agent simulation, reinforcement learning, and scalable oversight, including agent cognition, behavior analysis, reward design, and new training methods.
• Design state-of-the-art simulation and RL environments for training and evaluating frontier agents, spanning tools and actions, observations and state, trajectories, curricula, and reward systems.