General Machine Learning Engineer interview prep
12 live roles at SpotifyML algorithms, model deployment, MLOps and coding — the ML-engineering interview for AI-first product companies and applied-ML teams.
Typical Machine Learning Engineer interview rounds
- 1 ML fundamentals. Algorithms, evaluation metrics, bias-variance and overfitting.
- 2 Coding round. Python, data structures and implementing ML logic from scratch.
- 3 ML system design. Design a recommendation, ranking or fraud-detection system.
- 4 MLOps & deployment. Serving models, monitoring, drift and pipelines.
Commonly-asked Machine Learning Engineer questions
- Explain the bias-variance tradeoff and how it relates to overfitting.
- What is the difference between bagging and boosting?
- How does gradient descent work, and what are its variants (SGD, mini-batch, Adam)?
- Explain precision, recall, F1 and ROC-AUC, and when to optimise for each.
- What is regularisation (L1 vs L2) and why does it help?
- How would you handle an imbalanced dataset in a classification problem?
- Explain how a transformer / attention mechanism works at a high level.
- What is the difference between training, validation and test sets, and what is cross-validation?