Top Machine Learning Engineer Jobs in Seattle, WA

Lead the GenAI platform's quality and reliability efforts for Rovo Chat, focusing on evaluation, observability, and cross-team collaboration to improve outcomes.

Dandy seeks a Machine Learning Engineer to develop 3D computer vision models using SOTA methods, collaborate with product teams, and implement MLOps pipelines.

The role involves building computer vision models, researching deep learning methods, handling large datasets, and implementing MLOps best practices.

Lead the design and deployment of machine learning models for personalized navigation, collaborate across teams, and drive technical excellence in ML practices.

Develop and productionize a next-gen Dispatch System, build predictive models, automate processes, and lead a small team in optimizing operations.

Develop and productionize a next-gen Dispatch System, build ML models, automate processes, and collaborate with various teams while mentoring other engineers.

The Senior Applied Scientist II is responsible for designing and implementing ML models to optimize the supply chain, collaborating across teams, and driving data-driven decision-making.

Design and build ML features using LLMs, collaborate on technical architecture, and ensure safety and performance of deployments.

The Machine Learning System Engineer will develop and implement machine learning models, ensuring their effectiveness in various applications while collaborating across teams.

As a Senior Machine Learning Engineer, you will define search metrics, optimize retrieval paths, and investigate search-quality issues to enhance operational excellence.

Lead the development of ranking and relevance models, design ML systems for patient-provider matching, and mentor junior team members.

This role involves developing AI applications, focusing on LLMs and agent frameworks, and requires research skills in ML with experience in data strategy.

The Staff Machine Learning Engineer will define technical strategies, design ML models for document processing and risk assessment, and build scalable ML systems to protect Coinbase users' funds.

Lead the design and implementation of AI/ML models for risk and fraud detection at Coinbase. Collaborate with teams to develop scalable systems, mentor engineers, and ensure effective AI/ML solutions align with company goals.

The Research Engineer will develop and benchmark ML algorithms, optimize transformer architectures for edge devices, and collaborate on research projects across teams.

Lead the development of generative AI services and APIs at Adobe. Architect ML workflows, optimize inference pipelines, and drive cross-functional collaboration.

As a Machine Learning Engineer Intern, you'll enhance model performance, develop production-grade tools, and automate workflows while receiving mentorship from experienced ML professionals.

Adobe seeks a Machine Learning Engineer for applying AI techniques to enhance customer experience through predictive models and analytics. Responsibilities include developing scalable algorithms, collaborating on product features, and solving complex business problems using statistical and machine learning methods.

The position involves developing predictive models using machine learning techniques, collaborating with teams on product development, and optimizing algorithms for large-scale datasets.

As a Machine Learning Engineer, you'll develop APIs for generative models, optimize ML pipelines, and support enterprise-scale systems while collaborating with teams.

Design and develop generative AI services and APIs for Adobe products, optimizing models and building inference pipelines. Mentor ML engineers.

As a Principal Machine Learning Engineer, you'll develop advanced ML solutions using LLMs and Knowledge Graphs, leading teams and driving innovation in AI technologies for improved customer experiences.

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

Design and implement agent architectures, build LLM powered services, optimize infrastructure, ensure safety protocols, and collaborate across teams.

As an ML Services Engineer, you will design, deploy, and support AI-driven cloud services, optimize ML pipelines, and collaborate across teams for product development.