Agent Post-Training, Frontier Evals and Environments Research

Highlights

• Build north-star environments guiding OpenAI's most ambitious training runs.
• Create ambitious environments measuring frontier model skills and behaviors.
• Ship agent capabilities into Codex, ChatGPT and API products.

Ideal candidate

High-agency researcher who thrives in open-ended, noisy problems and cares deeply about useful, reliable agents. You blend research taste with engineering execution and want your work landing directly in frontier models.

Requirements

• Have strong technical fundamentals in machine learning, software engineering, systems, statistics, or a related field, and can learn quickly across the parts you have not worked in before.
• Have 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.
• Are excited by open-ended problems where the path is unclear, the signal is noisy, and the right answer requires both research taste and engineering execution.
• Care about product impact and model behavior, not just benchmark movement. You have opinions about what makes an agent useful, reliable, honest, tasteful, and easy to work with.
• Can move from a vague behavioral problem to a concrete experiment: define the hypothesis, build the pipeline, run the model, analyze the result, and decide what to do next.
• Are comfortable working across research, product, infrastructure, data, evals, and safety boundaries, and can communicate clearly with each group.
• Like building load-bearing systems and processes when that is what the team needs, even if the work is not glamorous.
• Want to train and ship the models that make agents genuinely useful for developers, enterprises, researchers, and everyday users.