Distributed Systems Engineer - Decisioning & Optimization

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.

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  will work across the stack from model serving to auction execution to pacing, shipping production systems that directly impact revenue and advertiser outcomes.

• 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‑20 ms P99 inference, including model routing, lifecycle management, fallback tiers, and calibration serving
• Partner with Science and Platform teams to product ionize 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

• 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, product ionizing ML models into low‑latency serving paths
• Ability to operate in an environment that is a mix of big‑tech scale and startup speed

• 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…