Member of Technical Staff - Research Scientist

Responsibilities As a Research Scientist 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. In this role, you will: Solve open-ended problems in scalable oversight, including agent cognition research, agent behavior analysis and new training methods for efficient RL. Develop state-of-the-art systems for agent simulation, evaluation and reinforcement learning environments used to test and train frontier models. 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. Publish papers, organize workshops and open source environments, datasets in collaboration with the evaluation research community. Independently scope, lead, and execute research projects end-to-end, from problem formulation and experiment design to results, write-ups, and production impact. Design and run rigorous experiments, set timelines, and consistently deliver measurable research outcomes such as papers, benchmarks, datasets, and platform features. Keep up to date with the latest research literature and technologies in scalable oversight; synthesize findings into clear research updates and concrete system improvements. 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. Collaborate closely with research, engineering, and product teams to translate research ideas into robust, real-world benchmarks and environments. Communicate progress clearly and proactively, flagging risks, blockers, or timeline changes early. Regularly review research progress with teammates, providing feedback, suggested next steps, and areas for improvement. Meet weekly with the CTO and researchers to align on priorities, progress, and next steps. Evangelize agent simulation and environment research internally and externally, contributing to Patronus AI’s thought leadership in scalable oversight. Qualifications "The number one qualification to succeed in this machine learning course is gumption” - John Lafferty, CS Professor at Yale Above all, we value intellectual curiosity, strong research instincts, and the ability to turn ambiguous ideas into concrete results. You are a strong fit if you have: 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.