Distributed Systems Engineer 6 - Decisioning & Optimization

• Experience with auction mechanics: first-price, second-price, reserve pricing, bid shading, and marketplace competition dynamics
• Multi-stage ranking systems (retrieval, scoring, reranking), podding and ad break planning
• Built or improved budget pacing and delivery control systems
• Yield optimization, inventory forecasting, dynamic pricing, fill rate optimization, and demand/supply allocation strategies
• Familiar with CTV constraints: server-side ad insertion, live event ad serving at scale
• Experience with experimentation infrastructure: A/B testing, holdout groups, interference-aware marketplace experiments
• Built simulation or counterfactual testing platforms for marketplace or auction systems
• Strong background in resiliency and reliability: ensuring system availability under extreme load (live events, traffic spikes)

Generally, our compensation structure consists solely of an annual salary; we do not have bonuses. You choose each year how much of your compensation you want in salary versus stock options. To determine your personal top of market compensation, we rely on market indicators and consider your specific job family, background, skills, and experience to determine your compensation in the market range. The range for this role is $499,000.00 - $900,000.00.

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.

Netflix is a unique culture and environment. Learn more here.

Inclusion is a Netflix value and we strive to host a meaningful interview experience for all candidates. If you want an accommodation/adjustment for a disability or any other reason during the hiring process, please send a request to your recruiting partner.

We are an equal-opportunity employer and celebrate diversity, recognizing that diversity builds stronger teams. We approach diversity and inclusion seriously and thoughtfully. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.

Originally posted on Himalayas

• 10+ years building distributed systems and backend services at large scale; 3+ years in the ads domain
• Deep experience with ML model serving infrastructure: scaling real-time inference on the hot path at high QPS with sub-20ms P99 latency, including model deployment pipelines, feature hydration, and fallback strategies
• Built and operated core ad tech systems: ad servers, bidders, pacers, or ranking and scoring components
• Designed APIs, platform abstractions, and data models that enable seamless interoperability across a multi-team ads platform
• Strong understanding of ad serving concepts: inventory management, frequency and recency capping, member ad experience quality, and supply-demand dynamics
• Track record of technical leadership across multiple teams, setting architectural direction and influencing cross-functional roadmaps
• Comfortable at the intersection of engineering, data science, and product, translating ML research and algorithms into production systems
• Demonstrated ability to operate in the environment which is a mix of big-tech scale and startup speed, taking projects that normally take years and delivering production-ready results with tight timelines