in November 2022 and are building an in-house world-class ad tech ecosystem to offer our members more choices in consuming their content. Our new tier allows us to attract new members at a lower price point while also creating a compelling path for advertisers to reach deeply engaged audiences.

The Ad Server Platform team sits within the Ad Serving & Decisioning org at Netflix Ads. We build and maintain the robust, scalable, and efficient ad serving infrastructure that forms the backbone of Netflix’s advertising ecosystem. Our mission is to ensure seamless and reliable delivery of ads across all platforms, devices, and viewing contexts.

Our work spans the core services and frameworks that the broader Ads Platform depends on: supply-agnostic controls, policies, frequency caps, applying floor prices, ad podding rules, competitive separation, rule-based targeting, event processing, and the logging frameworks. We are looking for a performance-minded systems engineer to ensure our ad serving infrastructure operates at peak efficiency under demanding latency and throughput requirements, serving teams across the ads ecosystem.

• Profile and optimize the ad serving runtime for latency, throughput, and resource efficiency across the full request lifecycle: targeting evaluation, policy enforcement, ad selection, and response serialization

• Identify and eliminate performance bottlenecks across services: CPU hotspots, GC pressure, memory allocation patterns, thread contention, and network overhead

• Design and run load tests, squeeze tests, and capacity models to validate system behavior under peak and burst traffic (including Live events at NFL scale)

• Establish performance baselines and regression detection: automated benchmarking in CI/CD to catch regressions before they reach production

• Instrument comprehensive latency telemetry, tracing, and profiling across the ad request lifecycle to enable data-driven optimization

• Optimize the rule engine framework and frequency management service for minimal overhead per request as policy complexity grows

• Work closely with the programmatic team on bid request/response performance: QPS management, connection pooling, timeout tuning, and load shedding under pressure

• Drive platform reliability through an efficiency lens: capacity planning, autoscaling tuning, graceful degradation, and cost-per-request optimization

• Own performance SLOs and budgets: define latency budgets per component, track them, and hold teams accountable when budgets are exceeded

• Partner with infrastructure and platform teams to adopt runtime improvements, evaluate hardware configurations, and tune GC strategies

• 7+ years building and optimizing distributed systems and backend services at scale

• Deep experience with performance engineering: profiling (async-profiler, JFR, flamegraphs), JVM internals (GC tuning, JIT compilation, memory models), and systematic bottleneck analysis

• Strong understanding of latency engineering: cache hierarchies, connection pooling, async I/O, thread pool sizing, and tail latency reduction

• Experience designing and running load tests, squeeze tests, and capacity models for high-throughput, latency-sensitive systems

• Built or operated ad servers, SSPs, DSPs, or real-time bidding infrastructure

• Proficiency in Java, Kotlin, or similar JVM languages with deep understanding of runtime behavior beyond just writing correct code

• Experience with event-driven architectures: Kafka, Flink, or similar stream processing, with a focus on throughput optimization and consumer lag management

• Understanding of ad serving concepts: targeting, frequency capping, publisher controls, programmatic protocols

• Ability to operate in an environment that is a mix of big-tech scale and startup speed

• Experience with CTV constraints: server-side ad insertion, live event ad serving at scale (NFL-sized audiences)

• Built or improved logging and telemetry frameworks for high-throughput request pipelines with minimal performance overhead

• Multi-region deployment experience: active-active architectures, failover, and regional performance variance analysis

• Chaos engineering or SRE practices: error budgets, game days, fault injection, squeeze testing

• Experience with hardware-aware optimization

• Built automated performance regression detection in CI/CD pipelines

Netflix provides comprehensive benefits including Health Plans, Mental Health support, a 401(k) Retirement Plan with employer match, Stock Option Program, Disability Programs, Health Savings and Flexible Spending Accounts, Family-forming benefits, and Life and Serious Injury Benefits. We also offer paid leave of absence programs. Full-time hourly employees accrue 35 days annually for paid time off to be used for vacation, holidays, and sick paid time off. Full-time salaried employees are immediately entitled to flexible time off. See more details about our Benefits here.