OpenAI Machine Learning Engineer Interview Questions

800+ verified questions, indexed by team and level. Real questions submitted by candidates who completed OpenAI loops in the last 24 months.

About the OpenAI Machine Learning Engineer hiring loop

OpenAI rounds are heavy on ML system design (training infra, inference at scale, RLHF pipelines) and deep technical depth in your specialty. Behavioural rounds probe research-product collaboration. Bar is unusually high; rejection rate ~98%.

ML Engineering rounds score on ML system design depth (latency, throughput, freshness, fairness trade-offs), training-infra fluency, and the bridge between model offline metrics and product KPIs. Coding is secondary to ML system thinking.

Topics covered in OpenAI Machine Learning Engineer interviews

  • 01ML system design (recommendations, ranking, search, fraud, ads)
  • 02Training infrastructure (distributed training, sharding, gradient sync)
  • 03Inference at scale (batching, KV-cache, quantisation)
  • 04Feature engineering and feature stores
  • 05Model evaluation (offline metrics vs online metrics, counterfactual)
  • 06Coding (Python, PyTorch / TensorFlow internals)

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