Distributed Systems Engineer 6 - 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 tierin November 2022 and are building an in-house world-classad tech ecosystemto 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 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 senior technical leader to own the technical direction of this pod, set the architectural bar, and drive execution on the hardest problems in ads optimization at Netflix . This is a 60% builder / 40% influencer role: you will write code, ship a proof-of-concept in your first weeks, and earn the trust of an opinionated senior team while simultaneously setting direction across the organization.
What You'll Do
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Own the technical direction of the Decisioning & Optimization team: architecture reviews, incident leadership, capacity planning, and scaling
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Architect and evolve the real-time ad decisioning optimization path: multi-stage auction, ranking, scoring, bidding, and pacing under strict latency and throughput constraints
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Scale our ads model serving infrastructure to support dozens of concurrent hot-path ML models with sub-20ms P99 inference, including config-driven model routing, multi-model lifecycle management, fallback tiers, and calibration serving
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Work closely with Science and Platform teams, ensuring seamless model productionization and algorithm deployment
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Build out various simulation and containerized testing frameworks to enable offline validation of marketplace changes before live rollout
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Design and implement real-time pacing systems that drive budget delivery accuracy across campaign lifetimes
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Develop and scale goal-based delivery optimization, enabling dynamic allocation of budget and inventory across multiple demand channels to maximize advertiser outcomes
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Drive modularization and platform-thinking: build reusable components and clean interfaces that let the team move faster
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Drive operational excellence: reliability, observability, deployment automation, capacity planning, and incident leadership across the optimization and broader ad serving stack
Skills & Experience We're Seeking
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10+ years building distributed systems and backend services at large scale; 3+ years in the ads domain
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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
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Built and operated core ad tech systems: ad servers, bidders, pacers, or ranking and scoring components
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Designed APIs, platform abstractions, and data models that enable seamless interoperability across a multi-team ads platform
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Strong understanding of ad serving concepts: inventory management, frequency and recency capping, member ad experience quality, and supply-demand dynamics
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Track record of technical leadership across multiple teams, setting architectural direction and influencing cross-functional roadmaps
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Comfortable at the intersection of engineering, da