Top Machine Learning Engineer Jobs in San Francisco, CA

Lead training, evaluation, and deployment of ML models for scene understanding and behavior prediction in autonomous vehicles.

You will train, evaluate, and deploy ML models for scene understanding and behavior prediction in autonomous vehicles, while collaborating with engineers.

Join Trexquant as a Global Alpha Researcher to develop trading signals, implement research, and apply machine learning for alpha discovery.

Lead the application of reinforcement learning techniques to enhance models for creative tasks. Require strong programming and machine learning project experience with a focus on user interface.

Train personalized ML models, create synthetic data pipelines, evaluate model quality, and develop safeguards for AI privacy and misuse.

As a Machine Learning Engineer, you will design and deploy advanced models for clinical reasoning, manage ML systems, and collaborate with teams to enhance healthcare delivery.

The Machine Learning Engineer role at Patreon involves developing machine learning models, analyzing data, collaborating across teams, and deploying solutions to enhance creator support.

Develop and productionize ML models for assessing autonomous vehicle behavior, build ML infrastructure, and collaborate across teams for deployment.

In this role, you'll develop machine learning models for logistics optimization, retailer underwriting, and growth strategies, collaborating with a skilled team to enhance the marketplace experience.

As a Senior Applied AI Engineer, you will develop and deploy machine learning models, improve system performance, and drive AI initiatives at Databricks.

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.