ML Engineer Interview Guide

Machine Learning Interview Questions

Evaluate ML engineers on modeling, evaluation, deployment, monitoring, and applied judgment around real production systems.

Strong ML candidates separate modeling cleverness from production realities: data quality, evaluation, drift, and operational ownership. These prompts focus on applied judgment.

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Section 1

Modeling

Test problem framing, feature engineering, and evaluation choice.

1. A team wants to predict customer churn. How do you frame the problem and choose evaluation metrics?

Interview prompt

Strong signal

Defines target carefully, picks appropriate metrics for class imbalance, considers business cost of errors.

Follow-up probes

  • •What baselines would you compare against?
  • •How do you handle survivorship bias?

Red flags

  • •Picks accuracy on imbalanced data.
  • •Ignores business cost of false positives or negatives.
Section 2

Deployment

Check serving, latency, and rollout judgment.

1. Design a serving system for a model that needs sub-100ms latency and frequent retraining.

Interview prompt

Strong signal

Discusses model registry, feature stores, shadow deploys, A/B testing, and rollback.

Follow-up probes

  • •How do you detect a bad rollout?
  • •What if the model regresses on a slice?

Red flags

  • •No rollback story.
  • •Ignores feature/training-serving skew.
Section 3

Monitoring

Evaluate drift detection, slice analysis, and lifecycle thinking.

1. How would you monitor a deployed model for silent failure?

Interview prompt

Strong signal

Talks about input distribution drift, output drift, calibration, slice metrics, and ground truth lag.

Follow-up probes

  • •What alert would actually page you?
  • •How do you handle delayed labels?

Red flags

  • •Only monitors latency and errors.
  • •Cannot reason about delayed feedback loops.

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