Top Machine Learning Engineer Jobs in Seattle, WA

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.

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.

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.

Lead design and implementation of MLOps platforms for pricing: productionize models, own end-to-end pipelines (ingest, training, deployment, monitoring), optimize data access and SQL, automate retraining and releases, improve reliability and observability, mentor engineers, and participate in on-call incident response.

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

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.

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 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 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.

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.

Lead technical direction and delivery of ML platform infrastructure (feature store, model serving, experimentation, training pipelines). Drive architecture, resolve system-level issues, write production code, mentor engineers, and partner with ML, data, and product teams to enable scalable, reliable ML model development and deployment.

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.

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.

Develop scalable machine learning services for data enrichment, managing the ML lifecycle from model training to deployment, and ensuring performance and reliability standards.

Lead applied research and engineering for feedback-driven agentic AI: define reward modeling and reinforcement learning roadmap, design production learning loops and evaluation frameworks, run large-scale experiments, and ship research into production while partnering across product and engineering.

As a Senior Staff Machine Learning Engineer at Affirm, you will lead complex ML system design and implementation, mentor engineers, and drive innovative ML strategies for financial services.

Lead design and operation of a large-scale feature store and serving systems for batch and low-latency ML inference. Drive architecture, storage and retrieval patterns, observability, on-call/incident response, and reusable feature registries while mentoring engineers and ensuring performance, correctness, and cost-efficiency.

Design, build, and scale end-to-end ML systems for distribution and supply: problem framing, data sourcing, feature engineering, model development, deployment, monitoring, and operational support. Collaborate with product and engineering teams, drive ML best practices (experimentation, observability, reliability), and mentor other engineers to improve ML engineering across domains.

The role involves designing and developing a distributed ML data platform for autonomous vehicles, collaborating with stakeholders, and mentoring engineers while ensuring operational excellence.

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 role involves leading machine learning initiatives to enhance automated testing for autonomous vehicles, improve model interpretability, and collaborate with cross-functional teams.