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 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 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 Own the technical direction of the Decisioning & Optimization team: architecture reviews, incident leadership, capacity planning, and scaling Architect and evolve the real-time ad decisioning optimization path: multi-stage auction, ranking, scoring, bidding, and pacing under strict latency and throughput constraints 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 Work closely with Science and Platform teams, ensuring seamless model productionization and algorithm deployment Build out various simulation and containerized testing frameworks to enable offline validation of marketplace changes before live rollout Design and implement real-time pacing systems that drive budget delivery accuracy across campaign lifetimes Develop and scale goal-based delivery optimization, enabling dynamic allocation of budget and inventory across multiple demand channels to maximize advertiser outcomes Drive modularization and platform-thinking: build reusable components and clean interfaces that let the team move faster 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 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 ... (truncated, view full listing at source)