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Netflix

Senior

Netflix L5 full-stack engineer building internal observability and visualization tools for ML/AI practitioners across personalization, ads, and content

This role is on Netflix's ML Insights & Visualizations team, building internal web platforms that let hundreds of AI/ML practitioners visualize, monitor, and operate models — including LLMs, bandits, and multi-task learning models — spanning UI, backend, and data layers. It's an L5 (senior) full-stack role focused on internal tooling and ML observability rather than consumer-facing product.

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这场面试考什么

  • Full-stack internal tooling: React/TypeScript/Node.js + Java/Scala/Spring Boot
  • ML/AI observability: model drift, anomaly detection, cost monitoring, system health dashboards
  • Designing developer-facing UX for ML practitioners
  • Legacy system modernization tradeoffs (refactor vs. rebuild vs. buy)
  • Cross-functional collaboration with ML engineers, data scientists, product
  • Cloud platform experience (AWS/Azure/GCP)

常见提问方向

Design an observability dashboard for monitoring ML model health/drift/cost at scale

A time you built an internal tool for a technical audience and iterated based on their feedback

How you'd instrument a pipeline serving LLMs or multi-task models for anomaly detection

Refactor vs. rebuild vs. buy: a legacy system modernization decision you made

Full-stack architecture choices connecting a React frontend to a JVM-based backend service

Collaborating across time zones with ML engineers, researchers, and product managers

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