Top Hybrid Machine Learning Engineer Jobs in San Francisco, CA

The Machine Learning Engineer Intern will assist with planning and control systems for autonomous vehicles, focusing on ML model performance and behavior decision processes, while collaborating with experts in the field.

As an Applied Scientist II, you will work with cross-functional teams on projects that optimize software systems and improve ML components, while analyzing inefficiencies and proposing solutions.

The Machine Learning Engineer will develop LLM-driven systems for information extraction and workflow automation, enhancing conversational AI technologies for insurance tasks.

The Applied Machine Learning Scientist will develop machine learning models to predict disease progression, communicate results, and contribute to modeling work in healthcare applications.

The role involves developing and deploying machine learning models, improving AI/ML infrastructure, and collaborating with cross-functional teams to drive business impact.

Design and implement high-performance CTR and CVR prediction models for ad ranking systems. Collaborate on system quality and direct future team strategies.

Develop custom models and algorithms for large datasets, collaborate with stakeholders to leverage data for business decisions, and assess data sources and techniques.

As a Machine Learning Engineer, develop ML models, data pipelines, conduct analytics, and work on image and video inference for fish health and behavior.

The Machine Learning Engineer II will develop and maintain AI/ML systems, manage cloud operations, and collaborate across teams to deploy ML-powered tools.

The Applied AI Software Engineer will evaluate LLM-based agents, manage benchmarking, and ensure performance, safety, and reliability in healthcare settings. Responsibilities include designing evaluation plans, running experiments, collaborating with teams, and developing internal tooling for AI features.