Jobs at Netflix

Own ads experimentation and incrementality measurement products for advertisers, agencies, and measurement partners. Partner with engineering, UX, data science, sales, legal, and other teams to define strategy, prioritize the roadmap, launch products, and drive adoption. Apply causal inference and experimentation methodologies, including A/B testing, conversion lift, brand lift, multi-armed bandits, search lift, and geo lift, while supporting Netflix’s advertising measurement ecosystem.

Build and operate scalable ML-powered advertising measurement platforms (Brand Lift, Incrementality, Attribution). Develop distributed data processing systems, productionize models, collaborate with data scientists, and implement backend services and data pipelines to deliver privacy-safe, scientifically rigorous ad measurement.

Design, build, and operate scalable, low-latency backend services for ad decisioning, targeting, pacing, and policy enforcement. Own APIs, data models, observability, capacity planning, incident response, and ensure platform reliability at massive scale while maintaining ad-tech domain expertise.

Design and build high-volume, low-latency Conversion APIs to ingest consented advertiser events (server-to-server, pixel, batch). Implement streaming architectures (Kafka/Flink), deduplicate and enrich identity/attribution data, enable measurement, optimization, retargeting and reporting while ensuring privacy, compliance, and cross-functional collaboration.

Design and build large-scale ad tech systems across inventory, serving, measurement, forecasting, identity, and campaign management. Drive architecture, set technical direction, collaborate with ad operations and cross-functional partners, and deliver high-performance, privacy-safe cloud services and data pipelines for Netflix Ads.

Design, build, and operate low-latency, high-QPS ML model serving and feature systems for real-time ad decisioning. Scale inference, ensure online/offline feature consistency, productionize ranking and scoring models, implement monitoring and simulation infrastructure, and partner with Data Science to validate and rollout models safely.

Design and architect AI-native, agentic platform systems for the Netflix Ads Suite. Define scalable UI patterns, schemas, and machine-consumable documentation; build AI-assisted prototypes and operationalize agentic workflows with engineering. Steward AI-generated outputs, mentor designers on AI fluency, and bridge Product Design with ML Engineering to ensure ethical, scalable, and performant platform infrastructure.

Develop, optimize, and deploy scalable predictive ML models to inform content strategy, valuation, scheduling, and performance. Own end-to-end model lifecycle (feature engineering, training, evaluation, deployment, monitoring) and partner with analytics, data engineering, MLOps, and content teams to scale production solutions and influence ML infrastructure.

Build and operate large-scale Ads Identity Graph and Identity Resolution systems (deterministic and probabilistic) in batch and near-real-time. Integrate identifier collection flows, DSPs, programmatic platforms, and data clean rooms while enforcing privacy, consent, and data governance. Handle extremely large datasets with big-data tools like Spark and design high-throughput, low-latency distributed systems. Collaborate cross-functionally on ad-targeting, measurement, and privacy-preserving solutions.

Lead design and execution of Netflix's advertising design system and core capabilities. Partner with central design system teams, research and define ads-related features (access, notifications, support), craft design briefs, and collaborate with engineers, product, data, and writers to build extensible React components and scalable workflows for global ad and live-streaming experiences.

Lead product development for Live event ad-serving infrastructure, handling unpredictable traffic, yield optimization across direct and programmatic demand, and cross-functional launches. Define metrics, iterate features, and partner with engineering, sales, ad ops, data science, and external partners to enable seamless Live ad delivery and drive advertiser outcomes.

Lead personalization product strategy and execution, partnering with ML scientists, engineers, designers, and research to improve recommendations. Define roadmap, prioritize experiments and evals, deliver consumer-facing ML products, and drive business impact across cross-functional teams.

Lead Netflix's global Physical Identity and Access Management program: design and operate PIAM systems, set policies, manage access credentials, integrate AI/biometrics, audit compliance, centralize access logs, collaborate with HR/IT/IAM partners, shape technology roadmap, track PIAM metrics, and produce documentation and training. Requires global stakeholder engagement and ~25% travel.

Build and scale programmatic ad-serving systems and integrations for Netflix Ads. Design low-latency infrastructure, enable PG/PMP deal workflows, implement experimentation and reporting, collaborate cross-functionally to launch and troubleshoot programmatic offerings.

Lead design and delivery of Ads Policy infrastructure: define architecture, governance, evaluation, and integrations for ad policy enforcement across campaign setup, creative review, serving, and reporting. Drive cross-team technical strategy, build scalable distributed systems, mentor engineers, and ensure operational excellence for production-critical ad systems.

Build and evolve a shared ads policy platform: authoring UIs, policy model and evaluation services, APIs, explainability, migrations from localized logic, and partner across product, policy, design, and engineering while mentoring teammates and owning end-to-end delivery.

Lead Pre-Sales and Revenue Management product strategy for Netflix Ads. Partner with sales, engineering, design, data science, and cross-functional teams to prioritize roadmap, launch revenue planning and inventory management products, leverage analytics to identify revenue opportunities, and drive adoption to maximize ad yield and monetization.

Lead research and development of generative AI and recommendation solutions for Netflix promotional assets and content discovery. Responsibilities include tracking LLM research, developing summarization and LLM-as-judge systems, training and evaluating models, optimizing generated content for audience impact, partnering with engineering teams, defining ML roadmaps, and communicating technical strategies to stakeholders.

Lead the Ads Signals & Measurement data engineering team across conversion and attribution, measurement foundations, audience onboarding, and identity. Set cross-organizational technical direction, resolve scalable data architecture decisions, mentor engineers, and partner on platform-wide initiatives. The role requires expertise in modern data stacks, advertising technology data flows, privacy and governance, distributed systems, and emerging AI technologies.

Own Talent’s knowledge governance strategy, taxonomy, information architecture, indexing, content lifecycle, and AI-readiness across a federated organization. Build standards, metrics, review rhythms, cross-business alignment, and AI-assisted content workflows. Partner with platform teams, guide business-unit adoption without direct authority, influence the Netflix Knowledge Platform roadmap, onboard additional business units, and establish the team needed to scale the function.