Distributed Systems Engineer 6 - Decisioning & Optimization

Netflix•Los Angeles, CA

About The Position

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.

Requirements

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

Nice To Haves

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 counterfactual testing platforms for marketplace or auction systems
Strong background in resiliency and reliability: ensuring system availability under extreme load (live events, traffic spikes)

Responsibilities

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