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.
Step into this interview
Free · a live voice mock calibrated to this exact role
What this interview tests
- 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)
Common question themes
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
Related interviews
Netflix
Senior
Integrations Support Engineer 5 - Ads Conversion API
Netflix
Senior
Software Engineering 5 - Ads Conversion Attribution
Netflix
Senior
Software Engineer 5, Ads Reporting
Cohere
Mid
Full-Stack Software Engineer, Inference
Cohere
Senior
Senior Full-Stack Engineer (Front-End Leaning), North (Agentic Platform)
Amazon
Mid