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

How candidates describe it

Real Performance Systems Engineer 5 - Ad Server Platform interview stories — retold from candidates' public write-ups, with sources.

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