Lead/ Senior Machine Learning Scientist, Integrative Phenotyping

Description

The company is a physical AI business that uses machine learning and large datasets to understand human biology and bring better medicines to patients sooner. Its internal platform finds new genetic points where a drug could intervene, and a second system turns those points into small molecule, biologic, or oligo therapeutics.

Programs in metabolic disease and neuroscience are moving toward the clinic, and the first IND submission is planned for the second half of this year. The company was founded in 2018 and has raised over $700 million from investors and pharmaceutical partners.

You will join the Integrative Phenotyping team as a Lead or Senior Machine Learning Scientist, reporting to the Head of Integrative Phenotyping. The team builds phenotypes from large multimodal human datasets, such as imaging, health records, outcomes, biomarkers, and multi-omic data, to understand patient state and find therapeutic targets. You will work with ML scientists, statistical geneticists, life scientists, software engineers, and clinicians, and help set standards for rigorous ML on complex human data.

Responsibilities

• Develop and deploy ML methods that pull biological insight from complex, high-dimensional cohort data, such as MRI and functional genomics.
• Build models that describe patient state and predict outcomes and clinical endpoints from imaging, health records, longitudinal outcomes, biomarkers, and multi-omic readouts.
• Make sure modeling approaches are rigorous, effective, and in line with current best practice.
• Build robust, reusable platform components with the software engineering team.
• Combine causal, multimodal analyses across different phenotypic readouts to choose better therapeutic targets.
• Work with Therapeutic Area and Clinical Development colleagues to create new biomarkers for patient selection and clinical endpoints.
• Work with the Statistical Genetics team to link human genetics to ML-derived phenotypes.

Requirements

• A 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, to pull clinically relevant signals from complex data.
• Experience with several of the following: clinical imaging such as MRI, DEXA, CT, or echocardiography; clinical records such as EHRs, medications, or outcomes; physiological recordings such as EEG or 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 publications in ML, ML for health, computational biology, or biomedical venues.
• Strong Python skills in a cloud environment and modern deep learning frameworks, with a record of reusable, tested, version-controlled ML code.
• Ability to take a real-world problem from sourcing and qualifying training data through model design, testing, and benchmarking, working with other ML scientists and engineers.
• Ability to collaborate with statistical geneticists, life scientists, software engineers, and clinicians, and to explain ML results clearly to each.
• Interest in the physiology behind the data. Prior exposure to cardiovascular disease, neuroscience, or metabolism is a plus.

Conditions

The target starting salary for successful US-based applicants is $183,000 to $224,000. Starting pay depends on factors such as skills, education, experience, market demand, business needs, and internal parity, and the range may change based on market data.

The role is hybrid and in person, three days per week at the South San Francisco headquarters.

The company is an equal opportunity employer and considers all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran status, or disability status.

Benefits

• Eligibility for the Annual Performance Bonus Plan, based on company targets by role level and annual company performance, and for the Equity Incentive Plan, subject to the terms of those plans.
• 401(k) plan with employer matching.
• Medical, dental, and vision coverage, plus 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 other new parents, including birth, adoption, and fostering.
• Quarterly budget for books and online courses.
• New hire stipend for a home office setup.
• Monthly cell phone and internet stipend.
• Free onsite coffee and daily lunch for onsite and hybrid employees.
• Free commuter bus network to the South San Francisco headquarters from locations around the Bay Area.