Meta Machine Learning Engineer Interview Questions

4100+ verified questions, indexed by team and level. Real questions submitted by candidates who completed Meta loops in the last 24 months.

About the Meta Machine Learning Engineer hiring loop

Meta's loop is two product-coding rounds, a system design (E5+), and two behavioural rounds against Meta's five values. Questions are filtered to role and indexed by org (Instagram, WhatsApp, Messenger, Reality Labs, core Meta, Ads).

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 Meta 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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