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

How candidates describe it

Real Software Engineer 4/5 interview stories — retold from candidates' public write-ups, with sources.

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