Top AI & Machine Learning Jobs in Seattle, WA

Lead and grow a team of ML and software engineers to build large-scale indexing, retrieval, and ranking systems for Snapchat ads. Define architecture and evaluation frameworks, collaborate with product and data science, measure outcomes, and raise engineering and ML standards while shaping Snap's broader ML strategy.

Lead research in user modeling and personalization, focusing on generative and language models, recommendation and retrieval systems. Build scalable prototypes, partner with engineering to deploy solutions, mentor junior researchers and interns, and publish work at top conferences.

Lead vision and roadmap for large-scale recommender systems across Spotlight, Discover, and Friend Stories. Technically lead engineering teams, design next‑generation recommender and retrieval architectures, incorporate SOTA research and foundation models, advance ML infrastructure for scalability and reliability, and partner cross-functionally to drive experimentation, operational excellence, and cost-effective production deployments.

The Staff Machine Learning Engineer will lead the development of search ranking systems, focusing on machine learning models for relevance, quality, and personalization through various advanced techniques and collaboration with cross-functional teams.

Lead technical strategy, architecture, and implementation for ML inference platform services. Design and scale distributed, high-throughput inference systems, collaborate across teams, drive availability, scalability, operational excellence, cost management, and provide company-wide technical direction and mentorship.

Design, build, and optimize large-scale ML infrastructure: embedding generation, batch inference, data storage/compute, data management, quality systems, and production deployments with ML engineers to improve ranking and recommendation systems.

Design, build, and deploy large-scale machine learning models powering Snapchat features. Own full ML lifecycle from data analysis to production, prototype with cross-functional partners, apply modern ML techniques, and use AI tools while ensuring code correctness, security, and scalable system design.

Design, build, and deploy large-scale ML models powering core products; own full ML lifecycle from data analysis to production; partner cross-functionally to prototype and launch features; apply modern ML techniques and AI tooling while ensuring code quality, security, and scalable production services.

Perform quantitative analysis, data mining, and statistical modeling to generate actionable product and business insights. Design and track core metrics, build dashboards and visuals, collaborate with cross-functional teams, use AI tools to scale analyses, and drive projects to completion to inform product decisions.

Lead product-focused quantitative analyses and machine learning efforts to generate actionable insights, drive product decisions, and optimize features. Collaborate with cross-functional teams to design metrics, build scalable statistical solutions, create dashboards, and communicate findings while using AI tools responsibly.