Machine Learning Engineer

The Machine Learning and Data engineer role 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 within UCSF’s APeX Enabled Research (AER) team. Most projects will be in partnership with other UCSF technical teams and involve highly customized research solutions. Communication skills and inventive technical solutioning are crucial. The AER team provides a large array of services to the UCSF Research community, including project consultation, grant support, budget estimations, and project implementation and support. Project examples include: * Development of EHR\-based interventions via clinical trials embedded within healthcare delivery systems to generate scientific evidence while delivering healthcare. + Enabling UCSF researchers with algorithms, digital tools and / or clinical interventions with strong evidence of feasibility and acceptability. + Develop technical approaches and budgets in order to implement these tools within the electronic medical record. + Supporting the development of scalable, low cost infrastructure to enable ongoing research. 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 6\+ years of experience in implementing and maintaining AI/ML pipelines