OpenAI
Mid
Train frontier models to produce polished, correct work artifacts — docs, spreadsheets, decks, dashboards
OpenAI's Agent Post-Training team is hiring a researcher/engineer to own end-to-end post-training improvements — RL, data pipelines, graders, reward signals, evals — that teach models to turn a vague user goal into a finished artifact with strong structure, taste, and correctness. Expect deep questions on RLHF/RLAIF, eval design, and translating messy model failures into concrete experiments.
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What this interview tests
- RL, RLHF/RLAIF, and post-training pipelines
- Eval and grader design for agentic/artifact-generation behavior
- Diagnosing model failures and turning them into training signal
- Data pipeline and reward-signal engineering
- Cross-functional research-to-product translation
- Experiment velocity, latency, and production readiness tradeoffs
Common question themes
Design an eval that exposes a specific artifact-quality failure mode (e.g., poor spreadsheet structure)
Turn a qualitative model failure into a concrete experiment and reward signal
Explain RLHF/RLAIF mechanics and where they break down for structured-output tasks
Debug a hard failure in a shipped or near-shipped model
Decide whether a capability improvement is ready to include in a major training run
Collaborate across research, product, infra, and safety boundaries
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