Staff ML Scientist, Integrative Phenotyping

The Opportunity

insitro's mission is to bring better drugs faster to the patients who can benefit most, through machine learning and data at scale. To address that goal, our discovery strategy integrates insights from multiple phenotypic readouts, spanning diverse high-content data modalities; we use data from public and proprietary human cohorts, and from in vitro cellular systems, generated by our proprietary, automated wet-lab platforms.

As a machine learning scientist on the integrative phenotyping team, you will develop, productionize, and deploy cutting-edge ML approaches to analyze and integrate large-scale multi-modal phenotypic datasets, including clinical imaging, electronic health records, physiological monitoring, longitudinal clinical data, biomarkers, and multi-omic modalities. You will work with clinical data from large human cohorts including national biobanks and other sources. You will contribute to developing models to understand patient state and predict outcomes and clinical endpoints. You will collaborate with a team of machine learning scientists, statistical geneticists, life scientists, and clinicians to identify therapeutic targets and develop drugs that have high efficacy and low toxicity. You will work in collaboration with software engineers to ensure these pipelines are robust, reusable components that can be deployed on large-scale datasets in a portable way. Your expertise will help the teams navigate the complexities of processing and cleaning high-quality data and ensure that the modeling strategies developed are performed to the highest rigor and in line with best practices in the field. You will report to the Director, Machine Learning, Integrative Phenotyping. We are open to both hybrid candidates local to the San Francisco Bay area and remote candidates for this role.

Responsibilities

• Develop and deploy ML methods for interpreting complex phenotypes, and for integrating multi-modal analyses across diverse phenotypic readouts
• Provide technical leadership within a team of outstanding machine learning (ML) scientists
• Collaborate with other leaders in the company to achieve important goals:
  • Therapeutic Area and Clinical Development colleagues to develop new biomarkers for patient selection and clinical endpoints
  • Statistical Genetics team to associate human genetics with clinical outcomes
  • Corporate Development team to identify and onboard new, high-value data sets to guide our analysis
  • Lab scientists and with other ML teams (Imaging, Omics) to design experiments that generate datasets that are fit for purpose for machine learning, including ones generated explicitly for training ML models
• Engineer robust, reusable platform components in partnership with the software engineering team

About You

• Expertise in developing and deploying state-of-the art ML methods for interpreting and extracting signals and clinically-relevant insights from complex data modalities, including hands-on experience with modern multimodal ML techniques
• Demonstrated ability to work with other ML scientists and engineers to plan, execute and deliver a full ML solution for challenging, real-world problems: sourcing and qualifying training data; designing and implementing ML models; testing & benchmarking
• 5+ years of experience with diverse clinical data modalities, including some subset of:
  • Imaging modalities (e.g., MRI, DEXA, CT, etc.)
  • Clinical records, including individual demographics, drugs administered, clinical outcomes, etc.
  • Physiological recordings (e.g., EEG, ECG, etc.)
  • Molecular readouts, including omics from blood or tissue
  • Large-scale human genetic datasets
• Experience with human physiology or disease biology (e.g., neuroscience, metabolism)
• Publication record of meaningful, high-quality contributions in relevant ML, ML for health, computational biology, or biomedical venues
• Experience producing high-quality, reusable ML code, including experience developing models using modern deep learning frameworks (such as PyTorch, TensorFlow, or Keras) and with cloud computing (preferably AWS)
• Experience mentoring, coaching, and leading junior scientists
• Passion for providing better medicine to patients in need!

Compensation & Benefits at insitro

Our target starting salary for successful US-based applicants for this role is $245,000 - $270,000. 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, insitro also provides our employees:

• 401(k) plan with employer matching for contributions
• Excellent medical, dental, and vision coverage (insitro pays 100% of premiums for employees on our base plans), as well as mental health and well-being support
• Open, flexible vacation policy
• Paid parental leave
• Quarterly budget for books and online courses for self-development
• Support to attend professional conferences that are meaningful to your career growth and role's responsibilities
• New hire stipend for home office setup
• Monthly cell phone & internet stipend
• Access to free onsite baristas and cafe with daily lunch and breakfast
• Access to free onsite fitness center
• Commuter benefits

insitro is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.

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.

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