University of California - San Francisco, CA
posted 2 months ago
The Machine Learning/Data Engineer at the University of California San Francisco (UCSF) will play a pivotal role in the development, implementation, and maintenance of data pipelines and infrastructure that support the deployment and continuous monitoring of Machine Learning (ML) and generative Artificial Intelligence (AI) tools within UCSF Health. This position is integral to the Health IT Platform for Advanced Computing (HIPAC), which is a cloud infrastructure designed to facilitate the development and deployment of AI/ML tools, including large language models (LLMs) integrated into the Electronic Health Record (EHR) system. In this role, the engineer will be responsible for managing and optimizing the data and monitoring pipelines that are essential for the effective functioning of HIPAC. Key tasks will include implementing new data integrations, enhancing the Extract, Transform, Load (ETL) functionalities of HIPAC, and productionizing AI/ML tools that have been developed by UCSF's data scientists and researchers. Additionally, the engineer will design and implement metrics to continuously monitor the performance and effectiveness of the AI/ML tools deployed at UCSF Health, ensuring that they meet the necessary standards and provide valuable insights. The ideal candidate for this position will have a strong background in software engineering, machine learning, or data engineering, with at least two years of experience in implementing and maintaining AI/ML pipelines. Proficiency in MLOps, Python, SQL, and Continuous Integration/Continuous Deployment (CI/CD) practices is essential. A deep understanding of Epic data models, specifically Clarity and Caboodle, is also required. Candidates should either possess or be able to obtain Epic Clinical/Clarity data model certification shortly after onboarding, which will further enhance their ability to contribute to the team effectively.