Distributed Systems Engineer - Decisioning & Optimization

Distributed Systems Engineer 6 - Decisioning & Optimization

New York, New York, United States of America
• Seattle, Washington, United States of America + 2 more

Remote

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 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 s 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.

• 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

• 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

• 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…