Agent Post-Training, Frontier Evals and Environments Research

Responsibilities

• Create ambitious RL environments to push frontier models to their limits and measure model capabilities, skills, and behaviors
• Develop new methodologies for automatically exploring model behavior
• Dive deep into the science of measurement, including scalability, reliability, and variance of evaluation methodology
• Help steer training for the largest training runs
• Design scalable systems and processes to support continuous evaluation
• Build self-improvement loops to automate model understanding

Requirements

• Strong technical fundamentals in machine learning, software engineering, systems, statistics, or a related field
• Hands-on experience with LLMs, RL, RLHF/RLAIF, post-training, evals, graders, synthetic data, model training, coding agents, tool-using agents, or production ML systems
• Ability to move from a vague behavioral problem to a concrete experiment: define the hypothesis, build the pipeline, run the model, analyze the result, and decide next steps
• Comfortable working across research, product, infrastructure, data, evals, and safety boundaries

Nice-to-Haves

• Excitement for open-ended problems where the path is unclear and the signal is noisy
• Care about product impact and model behavior beyond benchmark movement
• Opinions about what makes an agent useful, reliable, honest, tasteful, and easy to work with
• Willingness to build load-bearing systems and processes even when the work is not glamorous