Lead ML Scientist — Multimodal Phenotyping & Targets

Insitro; United States; Hybride; USD 183 000 - 224 000; Plein temps

Avantages offerts par ce poste

Annual bonus plan

Equity incentive plan

Excellent health coverage

Open vacation policy

Parental leave

Home office stipend

Commuter benefits

Résumé du poste

insitro in South San Francisco is seeking a Lead/Senior Machine Learning Scientist on the Integrative Phenotyping team to develop, productionize, and deploy ML methods that extract phenotypes from large multimodal datasets and use them to predict clinical endpoints and identify therapeutic targets. You will collaborate with ML scientists, geneticists, life scientists, engineers, and clinicians.

The role follows a three-day in-person hybrid schedule and reports to the Head of Integrative

Qualifications

• PhD in computer science, machine learning, or related field or 5+ years of applied ML research experience.
• Hands-on experience developing state-of-the-art ML methods, including multimodal techniques.
• Experience with clinical data such as imaging, EHRs, outcomes, or biobank-scale genetic data.
• Experience with high-content modalities and omics is a plus.
• Strong publication record in ML/health/biomedical venues.
• Proficiency in Python in a cloud environment and modern DL frameworks.

Responsabilités

• Build and deploy ML methods to extract biological insight from multimodal human cohort data.
• Model patient state and predict outcomes from imaging, EHRs, biomarkers, and omics.
• Ensure modeling strategies are rigorous and reproducible.
• Develop reusable platform components with software engineers.
• Collaborate with cross-disciplinary teams to translate ML results for biology.

Connaissances

PhD/5+yrs ML

Multimodal ML

Clinical Data

Python/Cloud

Publication Record

Engineering Fluency

Formation

PhD in CS/ML

Description du poste

insitro in South San Francisco is seeking a Lead/Senior Machine Learning Scientist on the Integrative Phenotyping team to develop, productionize, and deploy ML methods that extract phenotypes from large multimodal datasets and use them to predict clinical endpoints and identify therapeutic targets. You will collaborate with ML scientists, geneticists, life scientists, engineers, and clinicians.

The role follows a three-day in-person hybrid schedule and reports to the Head of Integrative