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Stanford University

ML Engineer – Healthcare Foundation Model Developer

450 Serra Mall, Stanford, CA 94305-2004

Hybrid · Full-time

First seen by offpeak October 4, 2026

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Stanford University is seeking a Machine Learning Engineer to support advanced technical research within the Weill Cancer Hub. This role will focus on developing and applying AI and machine learning methods for biomedical and clinical applications, with an emphasis on rigorous evaluation in real-world settings. The goal is to better understand the performance, safety, reliability, transparency, and clinical utility of ML/AI models intended for translational impact and eventual deployment.

 

Reporting to the program’s technical leadership, and working closely with senior faculty and research staff, the individual will build end-to-end data pipelines and software infrastructure for machine learning research across the Hub. They will develop robust, modular systems for training, evaluation, and inference that can support a range of downstream applications, and will help guide technical decisions around the languages, tools, platforms, and engineering practices used across data and software projects.

 

About Us:

The Department of Biomedical Data Science merges the disciplines of biomedical informatics, biostatistics, computer science and advances in AI. The intersection of these disciplines is applied to precision health, leveraging data across the entire medical spectrum, including molecular, tissue, medical imaging, EHR, biosensory and population data.

You Will Find This Position a Good Fit If:

Duties include:

 

* - Other duties may also be assigned

Core Duties:

Minimum Education and Experience

Bachelor’s degree in engineering, science, or related field and three years of relevant experience; or a combination of education and relevant experience.

Knowledge, Skills and Abilities:

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