Build the RL environments and evals that steer OpenAI's frontier agent training runs

OpenAI's Agent Post-Training team is hiring a research scientist to build north-star model environments and evaluations for agent capabilities like coding, tool use, computer use, and multi-agent coordination. This is a research-taste-plus-engineering-execution role: you define what 'good' looks like for a frontier agent, build the environment/grader/data pipeline to measure it, and steer real training runs. Prior team output includes GDPval, SWE-bench Verified, MLE-bench, PaperBench, and SWE-Lancer.

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What this interview tests

Designing RL environments / evals for agent capabilities (coding, tool use, computer use, multi-agent)
Grader design and validating measurement reliability/scalability/variance
Post-training methods: RLHF/RLAIF, synthetic data, model training pipelines
Cross-functional execution across research, product, infra, and safety
Turning ambiguous behavioral problems into concrete experiments

Common question themes

Describe an eval or environment you built and how you validated the grader wasn't gameable

How do you reason about scalability and variance in an evaluation methodology

Tell me about hands-on RLHF/RLAIF or post-training work you've shipped

How do you balance benchmark movement against real product/model-behavior impact

Describe aligning research, product, infra, and safety teams on a contentious decision

How would you design an environment to stress-test a coding agent's long-horizon reliability

How candidates describe it

Real Research Scientist interview stories — retold from candidates' public write-ups, with sources.

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