Top Machine Learning Engineer Jobs in Seattle, WA

Lead the delivery of ML initiatives, define roadmaps with cross-functional teams, and develop ML models for conversational agents and smart tools, enhancing customer support.

Lead design, implementation, and scaling of end-to-end ML systems including feature pipelines, model training, evaluation, deployment, and monitoring. Partner with cross-functional teams to productionize models, apply MLOps best practices (automated retraining, CI/CD, drift detection), and mentor engineers to maintain high code and system quality.

As a Staff Machine Learning Engineer, you'll enhance credit products through advanced modeling techniques, data analysis, and ML operations, supporting robust decision-making processes.

The Staff Machine Learning Engineer will develop machine learning models to enhance advertising strategies, improving relevance, ranking and customer engagement for Chewy's sponsored ads.

Lead design and development of scalable AI architectures and frameworks, collaborate with research and product teams, deploy and optimize ML/LLM systems in production, guide ML platform strategy, perform code reviews, mentor engineers, and evaluate emerging AI technologies for business impact.

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.

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.

Design, build, and operate production ML models, services, and platforms for pricing, risk, and decision systems. Own end-to-end model pipelines, deployment, monitoring, and iteration. Collaborate with researchers and product to productionize prototypes, tackle data drift and sparsity, lead design reviews, and mentor teammates.

The Staff Machine Learning Engineer will lead ML initiatives, partnering with multiple teams to enhance customer support through AI-driven solutions, focusing on conversational agents and research in chatbot architectures.

Lead end-to-end ML systems for identity verification, including document authenticity, face match, liveness and deepfake detection. Build GNN-based identity graphs, behavioral/device-intelligence models, vendor benchmarking/evaluation, and production enforcement. Mentor engineers and align ML architecture with Product, Compliance, Risk, and Security stakeholders.

Own the research-to-production pipeline: harden training/evaluation workflows, build packaging/deployment tooling, design release gates, optimize inference and serving for latency/throughput, establish benchmarking and validation, and instrument feedback from production to accelerate research iteration.

The Principal AI/ML Engineer will develop machine learning models for campaign optimization in AdTech, collaborating across engineering and product teams to enhance advertising capabilities.

Design, build, and deploy production-grade ML systems for ICS/xOT cybersecurity, including threat detection, anomaly detection, NLP/LLM applications, and scalable data pipelines with observability and MLOps practices for cloud and on-prem environments.

The Machine Learning Engineer develops production ML algorithms and recommendation systems, collaborates with teams, and optimizes user experiences.

Lead the machine learning strategy, design scalable ML systems, drive MLOps, mentor engineers, and comply with AI governance.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

Design, build, and operate scalable, low-latency search and retrieval systems powering Atlassian products. Develop and serve production ML models (neural rankers, embeddings, rerankers) using PyTorch and Triton. Implement vector indexing and RAG/agentic retrieval pipelines, ensure observability and cost optimization, manage on-call/runbooks, and collaborate cross-functionally while mentoring junior engineers.

The Staff Machine Learning Engineer will lead ML initiatives, develop ML systems, collaborate with cross-functional teams, and mentor junior engineers to optimize healthcare intake workflows using advanced machine learning techniques.

As a Machine Learning Engineer II, you will enhance AI systems for automating customer operations, develop evidence extraction pipelines, and collaborate with teams on model development and deployment.

Senior-level engineer delivering analytics and ML-Ops solutions for healthcare payers. Design and implement analytics platforms on Databricks, develop CI/CD and provisioning patterns, collaborate with data scientists, mentor juniors, and build client relationships to drive actionable insights and operational improvements.

The Machine Learning Engineer will develop and improve ML models for product surfaces, build data processing pipelines, and leverage machine learning technologies in advertisements.

The Staff Forward Deployed Engineer drives AI adoption for strategic customers by solving complex cloud infrastructure challenges, developing scalable assets, and influencing product roadmaps through collaboration and technical expertise.

As a Senior Machine Learning Engineer, you'll develop and optimize models for Chewy's Sponsored Ads to enhance product selection and advertising effectiveness. You will collaborate with cross-functional teams, lead model deployment, and publish research to elevate machine learning applications while mentoring junior scientists.