Show you can turn a fuzzy model-behavior question into a rigorous, falsifiable experiment

Interview prep for OpenAI's CoT Monitorability researcher role, which studies whether chain-of-thought reasoning in frontier models stays legible enough to support scalable oversight. This is empirical ML research, not just theory — expect deep dives on experiment design, evaluation-building, and how training interventions (pretraining, RL, post-training) affect monitorability. Strong candidates demonstrate hands-on LLM training/evaluation experience and can reason precisely about what evidence would or wouldn't support a monitorability claim.

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这场面试考什么

Empirical ML: training/evaluating/debugging large models
Designing falsifiable experiments from ambiguous questions
Chain-of-thought monitorability and scalable oversight concepts
How training interventions (pretraining, RL, post-training) affect monitorability
Distinguishing signal from noise in subtle experimental results
Translating findings into practical training/oversight recommendations