Uber Machine Learning Engineer Interview Questions

1300+ verified questions, indexed by team and level. Real questions submitted by candidates who completed Uber loops in the last 24 months.

About the Uber Machine Learning Engineer hiring loop

Uber's system design is the headline round — real-time location services, surge pricing, dispatch algorithms. Coding rounds are LeetCode-medium. Behavioural rounds anchor on the "Uber values" (refreshed in 2022) — Customer Obsession, Build with Heart, Stand for Safety.

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 Uber 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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