Top Machine Learning Jobs in San Francisco Bay Area, CA

The Principal ML Engineer at Agero will develop and productionize a Dispatch System using ML models, optimize decision-making processes, and lead a team to improve operational performance.

As a Senior Machine Learning Engineer, you will design and implement systems for ML and NLP, optimizing models and tools for Clover's healthcare solutions.

Lead the design, execution, and analysis of machine learning-driven product experiments to optimize ordering systems. Collaborate with stakeholders and mentor teams.

As an Applied Scientist at Lyft, you will develop ML and optimization models for dynamic pricing, collaborate across teams, and implement algorithms to enhance rider experience.

The role involves developing ML models, conducting experiments, and optimizing car assignments to improve Waymo's Marketplace efficiency.

Lead the development of production-grade machine learning systems, build ML pipelines, and optimize models for deployment and performance.

Lead and build a Machine Learning team, driving strategy and execution across product areas like Document Generation and Voice Agents while collaborating with Product and Engineering.

As a Staff Machine Learning Engineer, you'll design large-scale recommendation and retrieval systems, enhance quizlet's personalization through ML, collaborate with teams, and ensure models align with user outcomes.

Design and implement machine learning models to solve business problems, analyze data for insights, and optimize model performance.

Lead the Performance ML squad, managing and mentoring engineers while driving the development, deployment, and strategy of predictive models for advertising.

The AI/Machine Learning Specialist designs and implements AI solutions, collaborates with cross-functional teams, and continuously improves ML processes.

This role involves building and optimizing ML systems and frameworks, collaborating with teams to enhance research and development in ML models.

Develop and operationalize machine learning models for lab automation, leveraging multimodal data and ensuring reliable predictive capabilities in drug discovery processes.

Manage and grow the ML team, define goals and metrics, drive ML initiatives, build evaluation processes, and collaborate cross-functionally to enhance product delivery.

You will lead and manage a team of engineers developing machine learning solutions for perception product challenges in autonomous driving.

As a Machine Learning Intern, you'll prototype and evaluate ML systems, collaborate with teams, and analyze large datasets to improve user experiences.

Design, develop, and deliver machine learning models for clinical data. Collaborate with stakeholders and optimize production ML systems.

Lead a team of Machine Learning engineers, drive technical direction, oversee large-scale ML systems and improve user experience for over 500M users.

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

The role involves implementing ML models, building data pipelines, and quickly iterating on experiments in a team focused on causal intelligence for weather prediction.

As an AI/ML Engineer, develop and optimize analytics frameworks to enhance autonomous vehicle development, focusing on automation tools and collaboration with engineering teams.

Lead the design and deployment of machine learning models for personalized navigation and recommendations, managing projects end-to-end with a focus on user-centric solutions.

Design and deploy ML models for fraud detection, develop data pipelines, partner with teams to ensure effective fraud prevention, and enhance detection accuracy.

The Senior Software Engineer - Machine Learning at Celonis focuses on designing and developing AI/ML product features and customer solutions, ensuring high quality and scalability while collaborating with various teams.

The Senior Machine Learning Platform Engineer will build foundational components for ML models, mentor junior engineers, and optimize streaming pipelines. Responsibilities include enhancing ML infrastructure and ensuring data quality.