OpenAI
Mid
Scale compute-on-context research for OpenAI's frontier agents in Codex and ChatGPT
Interview for OpenAI's Agent Post-Training team, focused on scaling the compute spent on context as a lever for agent capability, with a concrete product surface in Codex Chronicle. Covers RL/post-training pipelines, evals and graders, and turning messy model-behavior failures into concrete experiments and fixes. Suited to candidates with hands-on LLM/RL/post-training or production ML systems experience who can move from a vague behavioral problem to a shipped fix.
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
- Compute-on-context scaling experiments
- RL/post-training pipeline design (data, rewards, graders)
- Building evals/environments that expose model failures
- Debugging model behavior and turning it into training fixes
- Cross-functional translation of research into shipped product improvements
常见提问方向
Design an experiment to test scaling compute spent on context
How would you build an eval or environment that surfaces a real agent failure mode
Walk through debugging a hard, messy model failure end to end
How do you decide a result is ready to go into a major training run
Balancing benchmark movement against real product/user impact
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