The machine learning/data engineer will lead the development, implementation, and maintenance of data pipelines and infrastructure to support the deployment and continuous monitoring of Machine Learning (ML) and generative Artificial Intelligence (AI) tools at UCSF Health. This role primarily involves managing and optimizing the data and monitoring pipelines of the Health IT Platform for Advanced Computing (HIPAC), a cloud infrastructure that supports the development and deployment of AI/ML tools, including large language models (LLMs) in the EHR. Specifically, the ML/data engineer will work on implementing new data integrations, enhancing HIPAC's ETL functionalities, productionizing AI/ML tools developed by UCSF data scientists/researchers, and designing and implementing metrics to continuously monitor AI/ML tools deployed at UCSF Health.
Competitive applicants for this position are software, machine learning, or data engineers with 2+ years of experience in implementing and maintaining AI/ML pipelines. Proficiency in MLOps, Python, SQL, and CI/CD is required. This role also requires a deep understanding of Epic data models (Clarity and Caboodle). Successful candidates either have or are able to obtain Epic Clinical/Clarity data model certification shortly after onboarding.
The final salary and offer components are subject to additional approvals based on UC policy.
Your placement within the salary range is dependent on a number of factors including your work experience and internal equity within this position classification at UCSF. For positions that are represented by a labor union, placement within the salary range will be guided by the rules in the collective bargaining agreement.
The salary range for this position is $125,500 - $188,300 (Annual Rate).
To learn more about the benefits of working at UCSF, including total compensation, please visit:
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