Top Remote Machine Learning Engineer Jobs in San Francisco Bay Area

The role involves designing, developing, and deploying machine learning models for healthcare applications, collaborating with cross-functional teams, and optimizing model performance.

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.

As a Staff Applied Machine Learning Engineer, you'll design and implement LLM applications, integrating ML features to enhance user experiences and scalable services.

The Staff Full Stack Engineer will enhance machine learning workflows at Cruise by developing UX experiences, backend integrations, and overseeing system architecture and performance metrics.

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

Design and build systems for knowledge retrieval and operational plans, optimizing information retrieval processes to support real-time decision-making.

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.

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

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

The role involves building machine learning models to detect risky activities, collaborating with cross-functional teams, and enhancing modeling capabilities. Candidates should have extensive experience in machine learning and strong analytical skills.

Lead development of advanced recommendation systems, driving ML innovation, maintaining ML pipelines, and collaborating across teams to enhance user engagement.

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

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

Lead R&D to design and deploy ML models for conversational agents, enhance automation in customer support, and build scalable AI systems across business units.

Lead the development of advanced machine learning models and AI solutions for customer support, focusing on chatbot and voice automation technologies.

The Senior Machine Learning Engineer will build AI-driven customer support products, collaborate cross-functionally, and maintain large-scale ML systems, leveraging modern techniques for real-world impact.

The Senior Machine Learning Engineer will develop scalable ML systems, collaborate across teams to improve solutions, and manage critical ML infrastructure projects.

As a Senior Machine Learning Engineer, you will design and enhance the AI evaluation platform, focusing on data pipelines and model benchmarking systems, collaborating closely with team members.

As a Senior Machine Learning Engineer, you will develop and scale machine learning models for Rovo Chat, ensuring reliability and utility, while communicating solutions across teams.

Develop and implement large-scale machine learning models to enhance user ad predictions and better understand user interests through data mining and collaboration with product teams.

The Machine Learning Engineer will scale core infrastructure, manage data pipelines and model evaluation, and improve benchmarking systems. Collaborate with teams to enhance AI evaluation methodologies.

As a Machine Learning Engineer, you'll optimize and deploy production ML models, build inference services, and collaborate closely with teams to integrate new models and enhance performance.

As a Machine Learning Engineer, you'll design and implement predictive analytics systems, optimize modeling processes, and maintain production systems while collaborating with teams.

You will build and maintain infrastructure for ML engineers, tackle complex architecture challenges, and lead projects from design to launch.

The role involves designing, optimizing, and deploying machine learning models for edge environments, focusing on computer vision. The engineer will collaborate with teams to integrate models and improve inference efficiency while mentoring peers and upholding company values.