Full-Stack Engineer 5 - 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\u2019s next.\n\nOur Team\n\nThe Decisioning \u0026 Optimization engineering team sits within the Ad Serving \u0026 Decisioning org 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.\n\nAs our systems grow in complexity and scale, we are investing in tooling and observability to make the decisioning stack fully legible to engineers, data scientists, and ad operations. We are looking for a full-stack engineer with strong analytical instincts and an observability mindset to build the tools and dashboards that make the stack debuggable, measurable, and self-service.\n\nWhat You\u0027ll Do\n\n * Design and build end-to-end internal tools and dashboards that give the team visibility into the ad decisioning stack, from model inference through different stages of auction\n\n * Build an ad decision debugger: trace the full path of an ad request (features, model scores, ranking, auction, delivery, billing) and surface why a particular ad was selected at a particular bid price\n\n * Build model serving observability: inference latency, score distributions, fallback rates, feature coverage, and calibration health across dozens of concurrent models\n\n * Build campaign delivery monitoring tools: spend tracking dashboards, frequency cap compliance views, pacing curve visualization, underspend and overspend alerts\n\n * Own the UI and BFF layer for experimentation and testing platforms, visualizing counterfactual results and offline vs. online comparison\n\n * Develop and maintain diagnostics, logging, and telemetry frameworks that provide deep visibility into system performance, model serving health, and campaign outcomes\n\n * Engage directly with engineers, data scientists, and ad ops to gather feedback and continuously improve the tooling experience\n\n\n\n\nSkills \u0026 Experience We\u0027re Seeking\n\n * 7+ years of professional software engineering experience building production systems, with meaningful full-stack experience across UI, BFF/API layer, and backend services\n\n * Proficiency in modern UI frameworks (React preferred), TypeScript/JavaScript, and Node.js\n\n * Experience building scalable backend systems in Java, Kotlin, or similar JVM languages\n\n * Built observability tooling, operational dashboards, or debugging tools for complex distributed systems\n\n * Strong analytical mindset with a bias toward building tools that enable self-service investigation and decision-making\n\n * Comfortable with data: can query, aggregate, and visualize large datasets across SQL, streaming data, and time-series metrics\n\n * Experience building tools that instrument or trace request paths through multi-service architectures\n\n * Product mindset that is deeply empathetic to user needs, strategic in orientation, and driven by outcomes\n\n\n\n\nNice to Haves\n\n * Ads domain experience: worked on ad serving, delivery, or marketplace systems and understands the operational data they produce\n\n * Built model serving monitoring tools: inference latency dashboards, score distribution tracking, fallback and calibration health views\n\n * Experience with observability platforms: metrics, logging, tracing stacks at scale\n\n * Familiar with marketplace dynamics: auction behavior, pacing anomalies, budget delivery patterns, and the tooling needed to diagnose them\n\n\n\n\nGenerally, our compensation structure consists solely of an annual salary; we do not have bonuses. You choose each year how much of your compensation you want in salary versus stock options. To determine your personal top of market compensation, we rely on market indicators and consider your specific job family, background, skills, and experience to determine your compensation in the market range. The range for this role is $320,000.00 - $500,000.00.\n\nNetflix provides comprehensive benefits including Health Plans, Mental Health support, a 401(k) Retirement Plan with employer match, Stock Option Program, Disability Programs, Health Savings and Flexible Spending Accounts, Family-forming benefits, and Life and Serious Injury Benefits. We also offer 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. See more details about our Benefits here. \n\nNetflix is a unique culture and environment. Learn more here.\n\nInclusion is a Netflix value and we strive to host a meaningful interview experience for all candidates. If you want an accommodation/adjustment for a disability or any other reason during the hiring process, please send a request to your recruiting partner.\n\nWe are an equal-opportunity employer and celebrate diversity, recognizing that diversity builds stronger teams. We approach diversity and inclusion seriously and thoughtfully. We do 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.\n