Top Tech Jobs & Startup Jobs in Seattle, WA

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.