Distributed Systems Engineer 6 - Decisioning & Optimization

Netflix; Seattle (WA); On-site; USD 250,000 - 420,000; Full time

Job summary

Netflix is seeking a senior technical leader to own the technical direction of the Decisioning & Optimization pod within Netflix Ads. You will write code, ship proofs-of-concept, and set architectural standards while guiding a senior team.

The role combines 60% hands-on building with 40% influencer work, focusing on real-time ad decisioning, architecture reviews, and scalable ML model serving across the ad stack.

Qualifications

• 10+ years building distributed systems and backend services at large scale.
• 3+ years in the ads domain.
• Deep experience with ML model serving infrastructure and real-time inference.
• Built and operated core ad tech systems like ad servers, bidders, pacers, or ranking components.
• Designed APIs and data models enabling interoperability across a multi-team ads platform.
• Strong understanding of ad serving concepts including inventory management and frequency capping.
• Track record of technical leadership and cross-functional influence.
• Comfortable translating ML research into production systems.

Responsibilities

• Own the technical direction of the Decisioning & Optimization team: architecture reviews, incident leadership, capacity planning, and scaling.
• Architect and evolve real-time ad decisioning optimization path: multi-stage auction, ranking, scoring, bidding, and pacing with latency constraints.
• Scale model serving infra for dozens of concurrent hot-path ML models with sub-20ms P99 latency, including routing and lifecycle management.
• Collaborate with Science and Platform teams to productionize models and deployment.
• Develop simulation and testing frameworks for offline validation before live rollout.
• Design and implement real-time pacing to drive budget delivery accuracy across campaigns.
• Drive modularization and platform thinking with reusable components and clean Interfaces.

Skills

Distributed systems

Backend services

Ads domain

ML model serving

Ad tech systems

APIs & data models

Latency & throughput

Technical leadership

Job description

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 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)