Top Senior Machine Learning Jobs in Seattle, WA

Design, implement, and productize scalable ML models and services. Build and maintain large-scale data pipelines and microservices. Collaborate with product and engineering teams, prototype solutions, lead design reviews, mentor junior engineers, and stay current with ML research to improve customer-facing features and platform capabilities.

The role involves deploying machine learning to enhance Chewy's advertising solutions, developing models for product ranking and prediction, and collaborating with product leaders. Responsibilities include leading new models, publishing research, mentoring, and driving technical strategies.

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.

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 the development of fraud prediction models using machine learning, collaborating across teams to build, scale, and monitor models in production.

Lead a team of ML engineers to develop fraud detection models, defining strategies, driving model evolution, and ensuring effective integration with cross-functional teams.

Lead the SDL and Cash Line ML teams, developing innovative machine learning models to improve lending solutions and expand credit access. Collaborate with cross-functional teams to drive product strategy and enhance model performance, while mentoring a team of ML scientists.

As a Principal Applied Scientist, you'll define the technical vision for offer optimization and conversion modeling systems, ensuring alignment across teams while overseeing large technical initiatives and contributing to real-world machine learning solutions.

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.

Manage a team of ML engineers to build and optimize Affirm's economic decisioning engine using advanced ML techniques. Collaborate across teams, provide technical leadership, and mentor engineers while driving a cohesive long-term ML strategy.

Lead a team of Machine Learning Engineers and Data Scientists to design and deliver ML systems at Remitly. Collaborate across domains to implement MLOps best practices and contribute to AI strategy.

Lead the development of machine learning systems for Cedar Pay's Personalization Engine, manage ML engineers, and ensure ML model effectiveness.

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 the development and implementation of machine learning solutions, managing a team to drive strategic decisions and innovative business outcomes.

Build and deploy transformer and sequential deep learning models using large-scale financial and behavioral data to power personalized growth and marketing experiences. Work cross-functionally to design scalable training, serving, and monitoring infrastructure (batch and real-time), contribute to experimentation and optimization frameworks, and maintain production-grade MLOps systems to improve member engagement and business metrics.

Design and implement ML algorithms for bidding and ranking systems in ads. Own complete ML systems from conceptualization to production. Collaborate with teams to integrate models and enhance advertising value using AI.

The Software Engineer will develop scalable, high-quality AI applications for environmental conservation, working on projects utilizing ML models and cloud technology.

Lead machine learning initiatives to enhance underwriting model accuracy. Design and implement advanced training strategies and optimize workflows. Mentor engineers and influence modeling strategy.

Build and own production applied-AI systems focused on information retrieval and LLM pipelines. Design experiment frameworks and evaluation pipelines, optimize for metrics (precision, recall, F1), run benchmarks, perform failure analysis, and iterate to improve real-world system performance at scale.

Design, build, and deploy transformer-based ML and LLM systems to automate risk investigations and decisioning. Own end-to-end pipelines (data ingestion, prompt tuning, orchestration, productionization, monitoring) and collaborate with product, engineering, and operations to deliver copilot/autopilot workflows that improve analyst productivity and eliminate manual reviews.

Develop and optimize machine learning models for data extraction from tax documents, collaborate on software integration, and mentor junior team members.

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

The Senior Machine Learning Engineer will develop ML APIs, build data pipelines, and collaborate across teams to enhance ML models and applications.

Design and build machine learning systems for Coinbase, responsibly use generative AI tools and copilots, apply human-in-the-loop practices, and deliver measurable efficiency, cost, and quality improvements while collaborating in a remote-first environment with periodic in-person surges.

Lead architecture and development of a SaaS platform for data center energy procurement. Build forecasting, contracting, and monitoring modules; implement AI tools on AWS (Bedrock, SageMaker); hire and mentor engineers; translate business logic into scalable systems; deliver demos to investors and customers.