Databricks Machine Learning Engineer Interview Questions

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

About the Databricks Machine Learning Engineer hiring loop

Databricks interviews are unusually deep on distributed-systems and big-data internals (Spark, Delta Lake, MLflow). Coding rounds are LeetCode-medium-to-hard, system design dives into lakehouse architecture, and behavioural rounds score "Customer Obsession + Truth-Seeking".

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 Databricks 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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