Top Healthtech Machine Learning Engineer Jobs in San Francisco, CA

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 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.

Lead the machine learning strategy, design scalable ML systems, drive MLOps, mentor engineers, and comply with AI governance.

Lead the development of machine learning systems for Cedar Pay's Personalization Engine, manage ML engineers, and ensure ML model effectiveness.

The Staff Machine Learning Engineer will lead ML initiatives, develop ML systems, collaborate with cross-functional teams, and mentor junior engineers to optimize healthcare intake workflows using advanced machine learning techniques.

Partner with a mentor to develop and apply ML methods on large multi-modal biological datasets (omics, imaging, genetics). Prototype, productionize, and deploy models, analyze results, and collaborate with cross-functional scientists over an 11–12 week summer internship.

The Staff Machine Learning Engineer will fine-tune LLMs for healthcare workflows, develop training datasets, create evaluation frameworks, and collaborate with experts to improve model behavior.

Citizen Health seeks AI/Machine Learning Engineers to develop AI solutions for healthcare using advanced ML techniques, focusing on rare diseases and patient care. Responsibilities include designing ML models, optimizing LLMs, and ensuring compliance.

As a Staff ML Engineer, you'll lead complex model quality improvements in clinical AI, spanning research and engineering, optimizing performance under high compliance conditions.

Develop innovative machine learning algorithms for modeling proteins, collaborate with biologists, and refine predictions through experiments.