Top Remote Machine Learning Engineer Jobs in Seattle, WA

The Machine Learning Engineer role focuses on utilizing machine learning techniques to enhance team productivity and develop software solutions. It involves working collaboratively to support various projects.

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

The Machine Learning Engineer will build and deploy ML models, applying modern techniques to solve problems and collaborate with teams on ML-driven features.

As a Machine Learning Engineer, you'll build and deploy models for core products, apply ML techniques to solve problems, and collaborate on features.

As a Staff Machine Learning Engineer, you will set the technical direction for the Ad Ranking team, design and deploy machine learning solutions, and mentor engineers while driving large cross-team initiatives.

As a Staff Machine Learning Engineer, you'll lead the Content Ranking team in enhancing machine learning models for video recommendations, ensuring scalability, performance, and technical direction across teams.

Lead complex AI/ML projects from conception to production, mentoring teams, performing data analysis, and establishing AI/ML best practices in healthcare.

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.

The Senior Staff Machine Learning Engineer will lead AI/ML initiatives, design scalable models, mentor engineers, and combat financial fraud through advanced methodologies.

The Principal Machine Learning Engineer shapes the machine learning strategy and architecture, leads initiatives, mentors engineers, and influences technical decisions at SharkNinja.

The Staff Machine Learning Engineer will lead model development to detect fraud, manage financial risks, and mentor other engineers. Responsibilities include system design, architecture, and applying AI/ML methodologies in a cross-team capacity.

As a Machine Learning Engineer in the Risk AI/ML team at Coinbase, you'll develop, enhance, and deploy ML models to prevent fraud and improve security for users. This role involves end-to-end model ownership, responding to threats, and collaborating with various teams to create automated defenses.

Develop and deploy machine learning models for risk assessment, collaborating with teams to enhance Gusto's product offerings while ensuring model performance and reliability.

Design and develop scalable AI/ML solutions while mentoring teams and collaborating with cross-functional partners to enhance customer experience through data-driven insights.

As a Machine Learning Engineer, you'll enhance ML productivity, improve model deployment, and implement algorithms for credit decisioning systems.

As a Staff Machine Learning Engineer, you will develop and maintain ML models for decision-making, optimize pipelines, and collaborate with a diverse engineering team.

As a Machine Learning Engineer, you will perform analysis on healthcare data, develop and deploy clinical machine learning models, and collaborate with various stakeholders.

As a Machine Learning Engineer, you'll design and implement scalable ML systems, develop data pipelines, support model experimentation, and collaborate across teams to deliver impactful ML solutions.

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.

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.

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.

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.

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