Top Tech Jobs & Startup Jobs in Seattle, WA

Build and maintain scalable data pipelines and data products to enable security and fraud detection. Implement ETL, data models, and distributed processing (Spark/Presto/Flink/Kafka) to centralize signals, support analytics and machine learning, and partner with security teams to improve detection and mitigation of fraud and account compromise.

Architect and implement system-level security for Open Connect edge and gaming appliances across hardware (FPGA, BMC, BIOS/UEFI), kernels and dual OS (FreeBSD/Linux). Improve platform integrity (measured/secure boot, BMC controls), triage system-level vulnerabilities, build low-level tooling and automation, and partner cross-functionally and with vendors to harden binary ingestion and execution.

Own protocol and cryptographic foundations for Netflix Open Connect edge appliances. Design and implement security primitives, optimize crypto/transport for performance, build DoS mitigations, conduct vulnerability research, and partner cross-functionally to deploy end-to-end security controls across delivery infrastructure.

Lead a software engineering team building privacy and data protection tooling across the data lifecycle. Define consumer-centric privacy solutions, partner cross-functionally, drive technical decisions, scale consent management, metadata catalog and protection controls, and measure risk mitigation and enablement outcomes.

Own and modernize end-to-end Workday-to-Talent integrations: design, build, deploy, and operate integrations; define standards; implement transformation logic; ensure observability, reliability, and vendor delivery; document and coach partners.

Lead and grow a team of ML scientists, data scientists, and analytics engineers to build bidding and pacing algorithms for Netflix Ads. Drive cross-functional partnerships with Product and Engineering, set roadmaps, ensure high-quality technical outputs, and translate business objectives into actionable analyses to optimize advertiser value and delivery across ad inventory.

Own metrics and datasets that measure cloud, storage, streaming, and ML infrastructure efficiency. Build end-to-end analytics: metric frameworks, pipelines, dashboards, and narratives that inform executive and engineering investment decisions. Pioneer methods to quantify AI and agent-driven productivity, identify efficiency opportunities, and partner with data engineers and data scientists to deliver trusted, reusable insights and recommendations.

Lead and deliver high-ambiguity, multi-team AI product programs from concept to production. Align engineering, applied science, product, and external partners; plan milestones, manage dependencies, and create decision-ready communications. Build lightweight program rituals, evaluate technical/business tradeoffs, and guide ML lifecycle execution for generative AI, search, discovery, and new content modalities.

Design, build, and maintain production-scale data systems and web-scale datasets (Spark/Flink) to support personalization ML. Drive dataset/schema design, collaborate with algorithm teams, and improve distributed big-data processing for member experience.

Build and prototype supervised ML models to predict ad campaign delivery outcomes (reach, frequency, contention). Own feature engineering, evaluation frameworks, interpretability, and partner with ML engineers to deploy and monitor models at scale. Collaborate across product, engineering, and sales to drive adoption and measure lift versus simulation baselines.