Distributed Systems Engineer 5 - Decisioning & Optimization

Distributed Systems Engineer 5 - Decisioning & Optimization

New York, New York, United States of America • Seattle, Washington, United States of America

At Netflix, our mission is to entertain the world. Together, we are writing the next episode - pushing the boundaries of storytelling, global fandom and making the unimaginable a reality. We are a dream team obsessed with the uncomfortable excitement of discovering what happens when you merge creativity, intuition and cutting-edge technology. Come be a part of what's next.

We launched a new ad-supported tier 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.

Our Team

The Decisioning & Optimization engineering team sits within the Ad Serving & Decisioning org at Netflix Ads. We own the systems that power real-time ad decisioning, delivering relevant, high-quality ads while balancing revenue goals, advertiser outcomes, and member experience. Our work spans ML model serving infrastructure, ranking and scoring, auction mechanics, budget and pacing systems, and goal-based delivery optimization along with podding, traffic shaping models, and more.

We are looking for a strong systems engineer to build and scale the core infrastructure behind ads optimization at Netflix. You will work across the stack from model serving to auction execution to pacing, shipping production systems that directly impact revenue and advertiser outcomes.

What You'll Do

• Build and evolve the real-time ad decisioning path: ranking, scoring, bidding, and pacing under strict latency and throughput constraints
• Develop and operate ML model serving infrastructure supporting dozens of concurrent hot-path models with sub-20ms P99 inference, including model routing, lifecycle management, fallback tiers, and calibration serving
• Partner with Science and Platform teams to productionize models and deploy algorithms into the serving stack
• Build simulation and testing frameworks to enable offline validation of marketplace changes before live rollout
• Implement and improve real-time pacing systems that drive budget delivery accuracy across campaign lifetimes
• Contribute to goal-based delivery optimization: dynamic allocation of budget and inventory across demand channels
• Build reusable components and clean interfaces that improve developer velocity across the team
• Participate in operational excellence: reliability, observability, deployment automation, and incident response across the optimization stack

Skills & Experience We're Seeking

• 7+ years building distributed systems and backend services at scale
• Ads domain experience (2+ years): worked on ad serving, delivery, or marketplace systems
• Experience with ML model serving infrastructure: real-time inference, model deployment pipelines, feature hydration, fallback strategies
• Built or worked on core ad tech systems: ad servers, bidders, pacers, or ranking and scoring components
• Built APIs and backend services that integrate across a multi-team platform
• Understanding of ad serving concepts: inventory management, frequency capping, member ad experience quality, and supply-demand dynamics
• Comfortable working at the intersection of engineering and data science, productionizing ML models into low-latency serving paths
• Ability to operate in an environment that is a mix of big-tech scale and startup speed

Nice to Haves

• Experience with auction mechanics: first-price, second-price, reserve pricing, bid shading
• Experience building multi-stage ranking systems (retrieval, scoring, reranking), podding and ad break planning
• Built or improved budget pacing and delivery control systems
• Familiar with CTV constraints: server-side ad insertion, live event ad serving at scale
• Experience with experimentation infrastructure: A/B testing, holdout groups, marketplace experiments
• Built simulation or counterfactual testing platforms for marketplace or auction systems

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 $388,000.00 - $619,000.00.

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.

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.