Distributed Systems Engineer 6 - Decisioning & Optimization

Netflix is hiring a Distributed Systems Engineer 6 for its Decisioning & Optimization team within Ad Serving & Decisioning at Netflix Ads. This is a senior technical leadership role for someone who will own the technical direction of the pod, set the architectural bar, and still write code. The position is based in the United States.

The team owns the systems behind real-time ad decisioning: delivering relevant ads while balancing revenue goals, advertiser outcomes, and member experience. Work spans ML model serving infrastructure, ranking and scoring, auction mechanics, budget and pacing systems, goal-based delivery optimization, podding, and traffic shaping models. Netflix describes this as a 60% builder / 40% influencer role. You will ship a proof-of-concept in your first weeks and earn the trust of an opinionated senior team while setting direction across the organization.

What you will 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 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 on model productionization and algorithm deployment.
• Build simulation and containerized testing frameworks for 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, allocating budget and inventory across multiple demand channels.
• Drive modularization and platform-thinking: reusable components and clean interfaces.
• Drive operational excellence: reliability, observability, deployment automation, capacity planning, and incident leadership across the optimization and broader ad serving stack.

What they are looking for

• 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.
• Have built and operated core ad tech systems: ad servers, bidders, pacers, or ranking and scoring components.
• Have designed APIs, platform abstractions, and data models that enable 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.
• A 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.
• Able to operate in an environment that mixes big-tech scale and startup speed, delivering production-ready results on 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.
• Have built or improved budget pacing and delivery control systems.
• Yield optimization, inventory forecasting, dynamic pricing, fill rate optimization, and demand/supply allocation strategies.
• Familiarity with CTV constraints: server-side ad insertion and live event ad serving at scale.
• Experience with experimentation infrastructure: A/B testing, holdout groups, interference-aware marketplace experiments.
• Have built simulation or counterfactual testing platforms for marketplace or auction systems.
• A strong background in resiliency and reliability, ensuring availability under extreme load such as live events and traffic spikes.

Pay and benefits

The compensation range for this role is $499,000.00 to $900,000.00 per year. Netflix states that its compensation structure consists solely of an annual salary, with no bonuses. You choose each year how much of your compensation you want in salary versus stock options. Netflix determines your personal top of market compensation using market indicators and your job family, background, skills, and experience.

Benefits include Health Plans, Mental Health support, a 401(k) Retirement Plan with employer match, a Stock Option Program, Disability Programs, Health Savings and Flexible Spending Accounts, Family-forming benefits, and Life and Serious Injury Benefits. Netflix also offers paid leave of absence programs. Full-time hourly employees accrue 35 days annually for paid time off to be used for vacation, holidays, and sick paid time off. Full-time salaried employees are immediately entitled to flexible time off.

Netflix states that inclusion is a company value and that it strives to host a meaningful interview experience for all candidates. If you want an accommodation or adjustment for a disability or any other reason during the hiring process, you can send a request to your recruiting partner. Netflix is an equal-opportunity employer and does not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.