Machine Learning Jobs in Bellevue, WA

Lead research and development of AI/ML-driven threat detection: analyze large datasets, build and validate proofs-of-concept, collaborate with threat and engineering teams, and help productionize scalable security detection systems.

Design, develop, and deploy advanced AI and optimization solutions for large-scale supply chain problems. Build and scale models for forecasting, inventory, network design, and routing; solve mixed-integer and stochastic programs; collaborate with engineering and product teams; engage customers and executive stakeholders; publish and evangelize supply chain science.

The Staff Technical Program Manager will manage simulation compute resources, develop execution strategies, and influence stakeholders for project success.

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.

The Staff Research Scientist will define research directions in AI and LLMs, develop models and systems for autonomous decision-making, and lead efforts to translate research into impactful solutions while mentoring team members.

Turn raw customer supply-chain data into structured representations and models. Interpret and contextualize noisy data, form and test hypotheses, build inference, regression, simulation and optimization logic, validate model quality in production, and translate scientific requirements into engineering specifications while collaborating with data engineers and supply-chain experts.

Build and maintain Pure1 Manage SaaS platform and web applications across frontend and backend. Develop subscription/commerce features, data pipelines, and ML-driven analytics. Use AI tooling for code generation, work with microservices, containers, Kubernetes, and collaborate with UX and stakeholders to take projects to production while owning testing, CI/CD, and code review.

The VP of Product will lead CoreWeave's Research Training Stack, focusing on enhancing AI infrastructure, ensuring quality for pre-trained models, and connecting researchers with advanced cloud capabilities.

Independently lead complex hardware and software programs from concept through production, managing scope, schedule, budget, suppliers, EMS, and BOM. Coordinate cross-functional hardware/firmware/mechanical/software teams, oversee validation/testing and regulatory certification, monitor program health metrics, communicate risks and status to stakeholders, and drive process improvements and team development to ensure successful product transitions to manufacturing.

The Architect for Finance and Legal Systems will develop technology strategies for enterprise systems, focusing on AI, ML solutions, and data integration across finance and legal applications to enhance operational efficiency and compliance.

As a Senior Software Engineer, you'll develop new features, improve existing components, mentor junior members, and tackle complex architectural challenges while leading projects autonomously.

As an Applied Scientist, you will develop ML and deep learning models for forecasting in advertising campaigns, collaborate with engineers and product teams, and continually improve model performance based on data-driven insights.

Lead pre-sales efforts for Media & Entertainment customers: present Snowflake to executives and technical audiences, deliver demos and POCs, design solutions, collaborate with product and engineering, and close large deals while maintaining deep industry and competitive knowledge.

The Project Support Coordinator will assist in data collection for AI and ML, guide participants, manage equipment, and respond to project updates.

Define and lead modeling architecture for personalization, matchmaking, recommendations, and social discovery. Build large-scale real-time inference systems, develop models for skill, behavior, trust & safety, and multi-objective optimization. Drive Responsible AI, experimentation, observability, and cross-functional standards while mentoring senior engineers and influencing ML strategy.

Lead design and delivery of production-grade ML pipelines and threat-detection models for cybersecurity. Drive rapid prototyping, MLOps, feature stores, CI/CD, and scalable streaming data processing while mentoring engineers and aligning detection strategy with security operations to reduce organizational risk.

Design and build end-to-end signal-to-model pipelines: large-scale feature engineering, identity prediction and embeddings, integrate outputs into ads targeting/bidding, own model lifecycle, ensure low-latency online serving, improve signal quality, run A/B tests, and provide technical leadership across Ads, Data, and Infra teams.

Design, build, and evaluate next-generation ranking and recommendation models using LLMs, RLHF, and preference learning. Develop user understanding systems for conversion and value estimation at massive scale. Apply reinforcement learning and optimization to bidding and auction dynamics. Design and run causal inference and A/B experiments. Partner with engineering to productionize models and communicate results to technical and non-technical stakeholders.

Design and operate large-scale data pipelines for ML training, integrating orchestration tools, optimizing performance, and leading architectural improvements.

The role involves building and maintaining machine learning infrastructure, including data pipelines and distributed training workflows, while collaborating with ML engineers to enhance efficiency and reliability.

As a Senior Machine Learning Infrastructure Engineer, you'll design and maintain the infrastructure for ML models, ensuring reliable performance and optimizing serving pipelines in a high-scale environment.

The Machine Learning Engineer will implement and evaluate ML algorithms, focusing on model development, A/B testing, and cross-functional collaboration to drive innovative solutions for financial challenges.