Member of Technical Staff - Applied Research

Patronus AI•San Francisco, CA

About The Position

As an Applied 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 society’s most impactful research questions surrounding agent simulations, and translate them into rigorous experiments. You will have the opportunity to work on reward design, tool simulations and behavior analysis, shaping the industry standard for robust, high quality environments. Your work will inform how frontier labs design, train and improve the next generation of agents for long-horizon tasks, progressing our path towards safe, human-aligned general intelligence.

Requirements

A BS, MS, or PhD in Computer Science, Machine Learning, Statistics, Mathematics, or a related quantitative field.
Experience conducting independent research in reinforcement learning, NLP, agentic systems, evaluation, or alignment.
Demonstrated ability to take open-ended research problems from 0→1 and deliver high-impact outcomes.
Strong experimental skills, including experiment design, analysis, and interpretation of results.
Experience writing clean, reproducible research code in Python and modern ML frameworks.
Ability to execute quickly with minimal guidance while maintaining high research quality.
Experience collaborating cross-functionally with research, engineering, and product teams.
Clear written and verbal communication skills, including the ability to explain complex ideas succinctly.
Strong integrity, good judgment, and respect for others.

Responsibilities

Solve open-ended problems in scalable oversight, including agent cognition research, agent behavior analysis and new training methods for efficient RL
Drive novel research in RL and environment design, including reward design, trajectory evaluation, interruption handling, observation and state design, tool and action interface design, and curriculum learning
Design and validate reward shaping strategies through reward hacking experiments to prevent exploits and ensure robust agent behavior
Evaluate and improve agent reasoning in complex, long-horizon workflows
Collaborate closely with engineering to productionize research, translating ideas into real-world, high-impact systems
Build world models and state-of-the-art simulation and RL environments to train and evaluate frontier agents
Advance agent cognition research across planning, adaptation, and generalization
Publish papers, organize workshops and open source environments, datasets in collaboration with the evaluation research community
Experiment with new models, techniques, and tooling, proactively proposing and running experiments to improve our understanding of agent behaviors, learning signal quality, and RL scaling
Produce high-quality, reproducible, production-level research code that integrates cleanly into Patronus AI’s systems
Evangelize agent simulation and environment research internally and externally, contributing to Patronus AI’s thought leadership in scalable oversight