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