Lead/ Senior Machine Learning Scientist, Integrative Phenotyping

insitro•South San Francisco, CA

4d•$183,000 - $224,000•Hybrid

About The Position

insitro is seeking a Lead/Senior Machine Learning Scientist for its Integrative Phenotyping team. The role develops, productionizes, and deploys machine-learning approaches to create precise phenotypes from large-scale multimodal human datasets—including clinical imaging, electronic health records, longitudinal outcomes, biomarkers, and multi-omic readouts—and uses them to characterize patient state, predict clinical endpoints, and identify therapeutic targets. The scientist will work with machine-learning scientists, statistical geneticists, life scientists, software engineers, and clinicians to advance rigorous machine learning on complex human data. The position is based in South San Francisco, reports directly to the Head of Integrative Phenotyping, and follows an in-person hybrid schedule of three days per week. It combines methods and platform development with target discovery and translation, including reusable software components, causal analysis, biomarker and endpoint development, and connecting human genetics with ML-derived phenotypes.

Requirements

PhD in computer science, machine learning, or a related field, or at least 5 years of equivalent applied ML research experience.
Hands-on experience developing and deploying state-of-the-art machine-learning methods, including multimodal techniques, to extract clinically relevant signals from complex data.
Experience with several types of clinical or human data, such as clinical imaging, clinical records, physiological recordings, or biobank-scale human genetic data.
Experience with high-content modalities such as single-cell or bulk omics, genetic perturbation screening, spatial proteomics, or pooled optical screening.
Meaningful, high-quality contributions in ML, ML for health, computational biology, or biomedical venues.
Strong Python skills in a cloud environment and experience with modern deep-learning frameworks.
Track record of creating reusable, tested, version-controlled ML code.
Ability to work collaboratively across scientific and engineering disciplines and communicate ML results clearly.

Nice To Haves

Prior exposure to cardiovascular disease, neuroscience, or metabolism.

Responsibilities

Develop and deploy machine-learning methods that extract biological insight from complex, high-dimensional human cohort data, such as MRI and functional genomics.
Build models to characterize patient state and predict outcomes and clinical endpoints using multimodal data, including imaging, EHRs, longitudinal outcomes, biomarkers, and multi-omic readouts.
Ensure modeling strategies are rigorous, effective, and aligned with field best practices.
Build robust, reusable platform components in partnership with software engineers.
Combine causal and multimodal analyses across diverse phenotypic readouts to select therapeutic targets.
Partner with Therapeutic Area and Clinical Development colleagues to develop biomarkers for patient selection and clinical endpoints.
Work with the Statistical Genetics team to associate human genetics with ML-derived phenotypes.
Collaborate with ML scientists and engineers to deliver real-world work end to end, from sourcing and qualifying training data through model design, testing, and benchmarking.
Communicate and make ML results understandable to statistical geneticists, life scientists, software engineers, and clinicians.

Benefits

Annual Performance Bonus Plan eligibility, based on company targets by role level and annual company performance.
Equity Incentive Plan eligibility, subject to plan terms and associated policies.
401(k) plan with employer matching for contributions.
Medical, dental, and vision coverage, as well as mental health and well-being support.
Open, flexible vacation policy.
Paid parental leave of at least 16 weeks for parents who give birth and 10 weeks for a new parent, including birth, adoption, or fostering.
Quarterly budget for books and online courses for self-development.
New-hire stipend for home office setup.
Monthly cell phone and internet stipend.
Free onsite baristas and daily lunch for employees who are onsite or hybrid.
Free commuter bus network to and from the South San Francisco headquarters from locations around the Bay Area.