Lead/ Senior Machine Learning Scientist, Integrative Phenotyping

5 дн. назад

СШАLeadHybrid

machine learningmultimodal dataclinical imagingelectronic health records

Develop, productionize, and deploy machine learning approaches to build precise phenotypes from large-scale multimodal datasets in a biotech context.

О компании

is a drug discovery and development company using machine learning (ML) and data at scale to decode biology for transformative medicines. At the core of ’s approach is the convergence of in-house generated multi-modal cellular data and high-content phenotypic human cohort data. We rely on these data to develop ML-driven, predictive disease models that uncover underlying biologic state and elucidate critical drivers of disease. These powerful models rely on extensive biological and computational infrastructure and allow to advance novel targets and patient biomarkers, design therapeutics and inform clinical strategy. is advancing a wholly owned and partnered pipeline of insights and therapeutics in neuroscience and metabolism. Since launching in 2018, has raised over $700 million from top tech, biotech and crossover investors, and from collaborations with pharmaceutical partners. For more information on , please visit.

Обязанности

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 .

Требования

Proven Tenure: PhD in computer science, machine learning, or a related field, or 5+ years of equivalent applied ML research experience.
Modern ML Expertise: Hands-on experience developing and deploying state-of-the-art ML methods, including multimodal techniques, to extract clinically relevant signals from complex data.
Clinical Data Depth: 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.
Multimodal Experience: Experience with high-content modalities such as single-cell or bulk omics, genetic perturbation screening, spatial proteomics, or pooled optical screening.
Publication Record: Meaningful, high-quality contributions in ML, ML for health, computational biology, or biomedical venues.
Engineering Fluency: Strong Python in a cloud environment and modern deep learning frameworks, with a track record of reusable, tested, version-controlled ML code.

Условия

Our target starting salary for successful US-based applicants for this role is $183,000 - $224,000 . To determine starting pay, we consider multiple job-related factors including a candidate's skills, education and experience, market demand, business needs, and internal parity. We may also adjust this range in the future based on market data.
This role is eligible for participation in our Annual Performance Bonus Plan (based on company targets by role level and annual company performance) and our Equity Incentive Plan, subject to the terms of those plans and associated policies.
In addition, also provides our employees:
401(k) plan with employer matching for contributions
Excellent 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 to support parents who give birth, and 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
Access to free onsite baristas and daily lunch for employees who are either onsite or hybrid
Access to a free commuter bus network that provides transport to and from our South San Francisco HQ from locations all around the Bay Area
We believe diversity, equity, and inclusion need to be at the foundation of our culture. We work hard to bring together diverse teams–grounded in a wide range of expertise and life experiences–and work even harder to ensure those teams thrive in inclusive, growth-oriented environments supported by equitable company and team practices. All candidates can expect equitable treatment, respect, and fairness throughout the interview process.
#LI-Onsite

Другое

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.
Integrate Causal Evidence: Combine causal, multimodal analyses across diverse phenotypic readouts to select better therapeutic targets.
Biomarkers & Endpoints: Partner with Therapeutic Area and Clinical Development colleagues to develop new biomarkers for patient selection and clinical endpoints.
Connect Genetics to Phenotypes: Work with the Statistical Genetics team to associate human genetics with ML-derived phenotypes.
End-to-End Delivery: You work with other ML scientists and engineers to take a real-world problem from sourcing and qualifying training data through model design, testing, and benchmarking.
Cross-Functional Translator: You collaborate well with statistical geneticists, life scientists, software engineers, and clinicians, and can make ML results legible to each.
Biology-Curious: You want to understand the physiology behind the data, and prior exposure to cardiovascular disease, neuroscience, or metabolism is a plus.