Netflix
Senior
Own latency and throughput budgets for the ad server that has to hold up during NFL-scale live events
This is a performance-engineering role on Netflix's Ad Server Platform team, owning profiling, bottleneck elimination, and capacity modeling for the ad-serving runtime (targeting, policy enforcement, ad selection, response serialization). The JD asks for 7+ years on distributed backend systems plus deep JVM/GC/profiling expertise and hands-on ad-serving or real-time-bidding infrastructure experience.
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What this interview tests
- JVM performance engineering: profiling, GC tuning, JIT, memory models
- Latency engineering: cache hierarchies, connection pooling, async I/O, tail latency
- Load/squeeze testing and capacity modeling for burst and live-event traffic
- Ad serving domain: targeting, frequency capping, rule engines, RTB/programmatic
- Performance regression detection in CI/CD and SLO/latency-budget ownership
Common question themes
Walk through your process for diagnosing a production latency regression in a JVM service
How would you capacity-model an ad server for an NFL-scale live event traffic spike
Describe optimizing a rule engine or frequency-capping service for per-request overhead
How do you define and enforce latency SLOs/budgets across dependent teams
Tell me about tuning GC or JIT behavior to fix a real production performance problem
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