Machine Learning Engineer interview
at Netflix.

Tell-me-about-a-time stories on ownership, conflict, and impact — scored against this company's real values.

1. 01 Tell me about a model decision you owned end-to-end and had to defend under repeated 'why' probing, embodying Netflix's high individual bar.
2. 02 Describe a time you challenged a popular modeling approach with candor and data, and how you drove the team to a better choice.
3. 03 Walk me through an ML system that underperformed in production, what you misjudged, and how you reflected and corrected course.
4. 04 Tell me about a time you chose a simpler model or pipeline over a more sophisticated one because it better served members.

The hard screen for the craft itself — talked through out loud, not whiteboarded in your head.

1. 01 How would you design a two-tower candidate-generation model with user and video embeddings, and how would you do hard-negative mining to avoid popularity bias?
2. 02 How would you build a multi-objective ranker that balances watch-time, freshness, and content diversity for the home screen?
3. 03 How would you architect incremental/online training on watch, impression, and abandonment signals while keeping the model stable?
4. 04 How would you evaluate a new recommendation model offline and then validate it online so the metrics actually predict member impact?

Open-ended design of the systems this company actually runs, with the interviewer probing your tradeoffs.

1. 01 Design Netflix's recommendation ranking system for the home screen, from event ingestion and a real-time feature store through candidate generation and ranking.
2. 02 Design the artwork/thumbnail personalization ML system that picks the best image per member and runs continuous A/B tests on it.
3. 03 Design a real-time feature store and serving pipeline that powers personalization for hundreds of millions of members at low latency.
4. 04 Design the end-to-end ML platform for training, deploying, and A/B testing personalization models with measurable business impact.