Netflix
Senior
Design and scale the ML systems behind Netflix's personalization and recommendation algorithms
Netflix's AI for Member Systems team builds the software that powers personalization algorithms across the Netflix experience, working closely with applied researchers and product managers to design production-ready ML systems, run offline experiments, and A/B test. This role is software engineering for ML at web scale — driving vision, design, and ownership of the components that keep recommendation algorithms reliable and iterable.
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What this interview tests
- Software engineering for large-scale ML/personalization systems
- Web-scale parallel and distributed computing (Spark/Flink-class systems)
- Production-ready design supporting offline experiments and A/B testing
- Collaboration with applied researchers/data scientists on production ML
- Engineering leadership and mentorship on best practices
Common question themes
Design a production system supporting a recommendation/personalization pipeline
Large-scale data processing design using Spark/Flink-style frameworks
Python plus Scala/Java/C++/C# coding round
Behavioral: driving software engineering best practices across a team
Discuss experience (if any) with ML model serving, training optimization, or LLMs
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