Build low-latency data products that power ML personalization and commerce at Netflix

L5 Distributed Systems Engineer on Netflix's Commerce Insights and Data Products Engineering team, building highly available, low-latency data products on the JVM stack (Java/Scala) that feed ML models and personalization across commerce and identity flows. Requires experience with large-scale distributed systems and batch/real-time processing frameworks like Spark or Flink, and comfort operating multi-tenant, high-throughput systems 24x7.

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Likely format

Netflix generally runs a recruiter screen, a hiring-manager/technical screen, then a virtual onsite loop covering system design, coding, and culture/values conversations.

What this interview tests

JVM stack (Java/Scala) and SQL proficiency
Distributed data systems for low-latency ML feature/inference serving
Batch and real-time processing frameworks (Spark, Flink)
Designing multi-tenant, high-throughput, 24x7-operable systems
Observability: monitoring, logging, alerting for proactive issue detection
Partnering with data scientists and product/business stakeholders under ambiguity