OpenAI
Mid
Research scientist shipping post-training improvements for OpenAI's agentic models
Interview for OpenAI's Agent Post-Training team, which builds the RL, data, evals, and reward-signal pipelines behind agents in Codex, ChatGPT, and the API. Expect open-ended research-engineering questions on turning vague agent-behavior failures into concrete experiments across coding, tool use, computer use, and multi-agent coordination.
走进这场面试
免费 · 一场按这个岗位校准的真语音模拟
这场面试考什么
- RL / post-training for LLMs
- Eval and environment design
- Agentic behaviors: tool use, computer use, multi-agent coordination
- Research-to-production experiment velocity
- Cross-functional translation of product signal into model training
- Calibrated reasoning and factuality
常见提问方向
Turning a vague agent failure mode into a concrete experiment and training fix
Designing evals/graders that reveal model weaknesses before shipping
RL/RLHF/RLAIF experience and reward-signal design tradeoffs
What makes an agent 'reliable and tasteful' — defend with a real example
Owning a post-training improvement from idea through integration and launch
Improving reliability, reproducibility, or cost of large training runs
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