Lead/ Senior Machine Learning Scientist, Integrative Phenotyping

insitro is a physical AI company dedicated to unlocking causal human biology and accelerating the delivery of better medicines to patients. Our unique Virtual Human™ platform identifies novel, high-impact genetic intervention points, which our TherML™ platform translates into therapeutics—whether small molecules, biologics, or oligos. With multiple programs in metabolic disease and neuroscience advancing toward the clinic, and our first IND submission slated for the second half of this year, we are at a pivotal inflection point.

As a Lead/ Senior Machine Learning Scientist on the Integrative Phenotyping team, you will develop, productionize, and deploy cutting-edge ML approaches to build precise phenotypes from large-scale multimodal datasets — clinical imaging, electronic health records, longitudinal outcomes, biomarkers, and multi-omic readouts — and use them to understand patient state, predict clinical endpoints, and identify therapeutic targets.

You’ll work alongside machine learning scientists, statistical geneticists, life scientists, software engineers, and clinicians, and your expertise will shape how the company builds rigorous ML on complex human data.

Based in South San Francisco, this position reports directly to the Head of Integrative Phenotyping and offers an in-person hybrid schedule of three days per week.

ML Methods & Platform

• Build Precise Phenotypes: Develop and deploy ML methods that extract biological insight from complex, high-dimensional human cohort data such as MRI and functional genomics.
• Model Patient State: Develop models that characterize patient state and predict outcomes and clinical endpoints from multimodal data including clinical imaging, EHRs, longitudinal outcomes, biomarkers, and multi-omic readouts.
• Set the Bar: Ensure modeling strategies are rigorous, effective, and aligned with best practices in the field.
• Engineer Reusable Components: Build robust, reusable platform components in partnership with the software engineering team.

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