Top Machine Learning Engineer Jobs in Seattle, WA

Lead the development of scalable ML infrastructure for fraud detection, collaborating across teams while mentoring peers and shaping technical strategy.

Lead design and implementation of enterprise-grade AI systems, architect AI infrastructure, and drive technical strategy for AI-powered products primarily for military applications.

The Principal ML Engineer will develop advanced machine learning systems, mentor junior engineers, and collaborate with cross-functional teams to optimize business solutions in Merchandising and Supply Chain.

The Senior ML Systems Engineer will develop a scalable ML data processing platform, leading projects, mentoring junior staff, and optimizing data systems across the cloud.

Join the Inference team to implement features and improvements for model serving on a GPU platform, focusing on performance and reliability.

As a Senior Machine Learning Engineer, you will build and optimize pipelines, ensure model quality, and collaborate with teams to enhance fraud detection systems.

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

Design and build a machine learning platform, deploy ML models, collaborate across teams, and establish monitoring standards for AI solutions.

The Senior Machine Learning Platform Engineer will develop ML models, mentor engineers, optimize systems, and enhance ML infrastructure at Coinbase.

The Senior Machine Learning Platform Engineer will optimize ML pipelines, mentor junior engineers, and improve ML infrastructure, focusing on performance and data quality.

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.

Research and optimize 3D rendering of sensor data, implement graphics algorithms, and collaborate to improve realism of simulations.

As a Senior ML System Engineer, you'll develop infrastructure for ML models, collaborate with product teams, lead projects, and mentor junior members.

Innovate core intelligence solutions within DevAI products, focusing on LLM integration, prompt engineering, and developing AI features in collaboration with diverse teams.

The Senior Machine Learning Engineer will lead AI coding agent design and deployment, optimizing ML pipelines and collaborating with product teams to enhance developer productivity.

Lead the architecture and management of AI/ML infrastructure using Kubernetes and Terraform, optimizing hybrid environments for performance and scalability.

Lead the design and optimization of advanced AI/ML systems at GEICO, mentoring teams, and ensuring architectural excellence while integrating innovative ML solutions.

Design and maintain scalable machine learning infrastructure, collaborating with data scientists, ensuring performance, security, and compliance of AI systems.

Join a team focusing on end-to-end ML Ops for the insurance sector. Develop and deploy ML solutions, build infrastructure, and solve business challenges using advanced technologies.

This role focuses on optimizing neural model architectures and systems for high performance across multiple GPU/TPU platforms, collaborating with various teams to enhance model efficiency and performance under real-time constraints.

The Principal Machine Learning Engineer will develop multimodal technologies, mentor engineers, and drive product innovation in agentic AI systems.

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

As a Machine Learning Engineer II, you will design, build, and deploy machine learning systems, optimize existing models, and collaborate with data teams.

As a Staff Machine Learning Engineer, you will design scalable ML systems, mentor engineers, solve complex problems, and optimize models in a fintech environment.

Lead the advancement of embedding-based retrieval systems, focusing on multimodal models, model performance, and extensibility for various use cases.