Lead/Senior Machine Learning Scientist, Integrative Phenotyping

insitro

. Develop, productionize, and deploy cutting-edge ML approaches to build precise phenotypes from large-scale multimodal datasets .

Posted 11/3/2026full-timeSouth San Francisco • California • United StatesSenior💰 $183,000 - $224,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Expertise in developing and deploying advanced machine learning methods for biomedical applications, with a strong focus on multimodal data analysis and collaboration across interdisciplinary teams. Proficient in Python and modern deep learning frameworks, with a proven track record of producing reusable and tested ML code.

Highest-signal resume keywords

PhD In Computer ScienceHands-On Experience With ML MethodsStrong Python In A Cloud EnvironmentProficiency With Deep Learning FrameworksExperience With Clinical Imaging

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningMultimodal TechniquesStatistical GeneticsComputational BiologyModel DesignData BenchmarkingHigh-Dimensional Data AnalysisBiomarker DevelopmentVersion-Controlled ML CodeCausal Analysis

Soft Skills

CollaborationCommunicationProblem-Solving

Tools & Technologies

Deep Learning FrameworksCloud EnvironmentEHRsMRISingle-Cell OmicsSpatial ProteomicsPooled Optical Screening

Industry Keywords

BiomedicalClinical DevelopmentTherapeutic TargetsHuman GeneticsCardiovascular DiseaseNeuroscienceMetabolism

Tech Stack

Tools & technologies

CloudPython

About the role

Key responsibilities & impact

Develop, productionize, and deploy cutting-edge ML approaches to build precise phenotypes from large-scale multimodal datasets
Extract biological insight from complex, high-dimensional human cohort data such as MRI and functional genomics
Develop models characterizing patient state and predicting outcomes and clinical endpoints
Ensure modeling strategies are rigorous, effective, and aligned with best practices
Build robust, reusable platform components with the software engineering team
Combine causal, multimodal analyses across diverse phenotypic readouts to select better 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 machine learning scientists, statistical geneticists, life scientists, software engineers, and clinicians
Report directly to the Head of Integrative Phenotyping

Requirements

What you’ll need

PhD in computer science, machine learning, or a related field, or 5+ years of equivalent applied ML research experience
Hands-on experience developing and deploying state-of-the-art ML methods, including multimodal techniques
Experience with several of: clinical imaging (e.g., MRI, DEXA, CT, echocardiography), clinical records (e.g., EHRs, medications, outcomes), physiological recordings (e.g., EEG, ECG), 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 in a cloud environment
Proficiency with modern deep learning frameworks
Track record of reusable, tested, version-controlled ML code
Ability to take real-world problems from sourcing and qualifying training data through model design, testing, and benchmarking
Ability to collaborate with statistical geneticists, life scientists, software engineers, and clinicians and make ML results legible to each
Prior exposure to cardiovascular disease, neuroscience, or metabolism is a plus

Benefits

Comp & perks

Annual Performance Bonus Plan
Equity Incentive Plan
401(k) plan with employer matching for contributions
Excellent medical, dental, and vision coverage
Mental health and well-being support
Open, flexible vacation policy
Paid parental leave of at least 16 weeks for parents who give birth
Paid parental leave of 10 weeks for a new parent, inclusive of birth, adoption, fostering, etc.
Quarterly budget for books and online courses for self-development
New hire stipend for home office setup
Monthly cell phone & 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 HQ from locations around the Bay Area