Top Senior Machine Learning Jobs in San Francisco Bay Area, CA

The Senior Machine Learning Modeler will lead the design and implementation of AI-driven models for forecasting and financial planning, collaborating across teams to create scalable ML architectures and tools for model interpretability.

The Machine Learning Modeler will design and scale intelligent systems, build forecasting models, and collaborate with analytics teams to make data-driven decisions at Block.

The Senior Software Engineer will lead projects and develop AI/ML features using Python and Go, focusing on model services and APIs.

Develop and implement advanced reinforcement learning methods and distributed systems for AI agents. Collaborate with customers to understand needs and create tailored solutions.

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

Develop machine learning models for search ranking and personalization at Faire, leveraging data to help local retailers thrive.

Lead the design and deployment of machine learning models to detect fraud, collaborating with teams to optimize performance and effectiveness while integrating solutions into existing systems.

The Staff Machine Learning Engineer will architect and optimize data infrastructure for multimodal AI models in oncology, focusing on data management and processing pipelines.

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.

Lead the development and execution of ML platform strategies, manage a high-performing team, and enhance 3D deep learning applications.

This role involves designing and building advanced ML models to protect against fraud and enhance user experience while mentoring lower-level engineers and influencing technical roadmaps.

Own the full ML lifecycle for ads bidding stack, developing models for auction optimization and collaborating with a team to enhance platform effectiveness.

As a Principal Machine Learning Engineer, you will develop and implement advanced ML algorithms, collaborate across teams, and mentor junior engineers, driving AI integration in Atlassian products.

The Lead Machine Learning Engineer will innovate AI systems, collaborating with various teams to ideate and implement impactful solutions for Salesforce customers.

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

As a Senior Machine Learning Engineer, you will develop scalable ML solutions, collaborate with teams, optimize models, and implement best practices for deployments and infrastructures.

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.

The Senior Machine Learning Engineer will develop ML APIs, build data pipelines, and collaborate across teams to enhance ML models and applications.

As a Senior Machine Learning Engineer, you will design ML infrastructure, lead zero-to-one initiatives, collaborate on projects, and enhance scalability in the development of ML applications.

The Senior Machine Learning Engineer will design and implement ML systems, optimize data pipelines, and collaborate on ML workflows for personal injury legal solutions.

The Staff Machine Learning Engineer will design ML systems, mentor engineers, work on AI product development, and improve legal data processing.

The role involves developing ML techniques for code security, training models, building evaluation pipelines, and collaborating on research innovations.

As a Research Engineer, you'll design and deploy advanced machine learning models, collaborate with experts, optimize data pipelines, and ensure models deliver value by implementing cutting-edge AI solutions.

As a Staff Machine Learning Engineer, you'll lead ML system strategy for fraud and risk management, use analytics for decision-making, and drive technical solutions across teams.

Design and implement ML models to combat financial crimes, collaborate with stakeholders, and maintain regulatory documentation while leading ML practices.