Top Machine Learning Engineer Jobs in San Francisco, CA

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

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

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

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

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.

Develop mathematical models for discounting systems, collaborate on algorithms, analyze data, and improve rider engagement products while working with cross-functional teams.

The Applied AI Engineer will design and implement AI/ML solutions for operational efficiency in manufacturing and supply chain, collaborating with various teams to drive improvements.

The Machine Learning Engineer at Lyft will design, build, and deploy ML models across various domains, collaborating with data scientists and product managers to solve complex business problems at scale.

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.

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

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.

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.

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 VP of Ads Quality Engineering will lead the AI/ML engineering team to improve Pinterest's ads delivery and monetization strategies, collaborating with various departments to develop innovative solutions using machine learning.

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.

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

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.

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.

As a Senior Machine Learning Engineer, you will develop machine learning models, manage their lifecycle, build infrastructure, and collaborate with teams to enhance our capabilities in various ML areas.

The Applied Scientist will transform raw data into actionable insights by developing machine learning models for personalization, ranking, and optimization to enhance conversion and revenue. This role emphasizes building production systems, frameworks for experimentation, and communication with team members regarding technical challenges.

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

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

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