Top Machine Learning Engineer Jobs in San Francisco, CA

As a Staff Machine Learning Engineer, you'll lead AI product development, leveraging LLMs and embeddings, collaborating on technical strategy and coding performance enhancements.

The Engineering Manager, Machine Learning will lead a team focusing on 3D deep learning and computer vision, manage the ML roadmap, and implement best practices for MLOps pipelines.

Lead the design and implementation of machine learning applications, collaborate with cross-functional teams, optimize ML systems, and ensure responsible AI practices.

As a Machine Learning Engineer, you'll develop and ship models for an AI platform, own projects from start to finish, and work closely with customers to enhance product features.

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.

The Staff Machine Learning Engineer will develop classification systems for content and creators, building machine learning pipelines, conducting analyses, and collaborating with cross-functional teams for effective solutions.

The Machine Learning Engineer will propose technical initiatives, lead design discussions, translate challenges into plans, and uphold engineering standards.

As a Machine Learning Engineer, you will develop and deploy adaptive ML systems, collaborate with partners, and lead product strategy.

The Scientist, AI Engineer role involves designing and deploying machine learning models for drug discovery, collaborating with cross-functional teams, and leveraging proprietary data to build AI-powered tools for medicinal chemistry and biology.

The Staff Machine Learning Engineer will lead the design and implementation of conversion models, establish ML best practices, and collaborate with teams to enhance Pinterest's ad performance.

Design, build, and ship ML-powered product features (search, personalization, recommendations, computer vision). Improve ML platform, pipelines, deployment, monitoring, and data infrastructure. Collaborate cross-functionally to define metrics, iterate models, and deliver scalable, reliable ML systems.

Develop and maintain a full-stack platform for computational chemists, focusing on drug discovery through machine learning and data infrastructure. Collaborate with interdisciplinary teams to enhance software tools for visualizing molecules and managing workflows.

Lead machine learning initiatives to enhance underwriting model accuracy. Design and implement advanced training strategies and optimize workflows. Mentor engineers and influence modeling strategy.

The Machine Learning Engineer will work on unstructured data classification and clustering, utilizing their expertise in AI systems and various programming languages.

The Senior Machine Learning Engineer will utilize deep experience and insights to enhance communication and digital interactions through advanced machine learning tools and algorithms, focusing on unstructured data.

Lead development and production deployment of advanced ML and time-series models for water and infrastructure systems, perform research in Physical AI, collaborate cross-functionally, communicate impact to stakeholders, and mentor junior data scientists.

As a Senior Machine Learning Engineer, you will design and build ML models for patient-provider matching, focusing on personalization and ranking. Responsibilities include model development, A/B testing, and collaborating across teams to improve patient experiences.

The intern will work on projects involving revenue optimization and risk mitigation for solar and storage projects, utilizing machine learning and optimization techniques.

The ML Data Engineer develops and maintains machine learning pipelines and systems, collaborating with teams to optimize manufacturing processes through data-driven insights and predictive models.

The role involves designing ML systems for Search Ads, focusing on improving ad relevance and personalization with large language models and deep learning.

Lead the development of machine learning models for ad bidding at Faire, optimizing performance and influencing marketplace strategy.

Lead the Applied Machine Learning team at ExtraHop to develop systems for behavioral detection in network security. Manage model design, evaluation, and operationalization while maintaining experimental rigor and collaborating with product teams.

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

Intern will work on ML model experimentation and integration for improving perception systems, focusing on real-world application and collaboration.