About the role
The Opportunity insitro's mission is to bring better drugs faster to patients who can benefit most, through machine learning and data at scale. Our discovery strategy integrates insights from multiple phenotypic readouts across diverse high-content data modalities, including data from public and proprietary human cohorts and in vitro cellular systems generated by our proprietary, automated wet-lab platforms. In this role, you will lead and grow a team of exceptional machine learning researchers responsible for co-developing methods for extracting information from in-vitro biology using computer vision and machine learning. You will lead an established team of 5 machine learning scientists, several at the Staff level, and be expected to grow it over time. Your team will partner closely with laboratory scientists to develop biological assays in a tight loop, iterating sample preparation protocols and feature extraction methods in tandem. As the manager of this team, you will be responsible for structuring these collaborations in ways that ensure the success of all teams, and your individual team members, as measured by our ability to drive insitro's therapeutic programs forward through understanding of causal human biology. In addition to working closely with the lab, you and your team will partner closely with our imaging software team, responsible for co-developing and scaling methods your team develops, and computational biologists who will subsequently leverage these tools to extract program-level insights in conjunction with omics (perturb-seq) and clinical insights. As the leader of a team of researchers within the AI/ML organization, you will be expected to remain abreast of the evolving field of in-vitro screening. You will report to the Director of Imaging, Cellular Machine Learning . This is a hybrid position that requires you to be in our South San Francisco headquarters at least three days per week . Join us, and help make a difference to patients!
Responsibilities Lead, mentor, and grow a team of outstanding machine learning scientists to co-develop methods of extracting insights from in-vitro microscopy datasets Work closely with laboratory leadership to structure assay development efforts, and empower members of your team to co-develop in-vitro microscopy assays suitable for genetic perturbation screening, often at whole-genome scale Ensure the successful translation of assays you develop to insitro’s computational biology team, who will leverage the assays your teams develop to investigate causal biology through the integration of diverse data modalities, including microscopy Co-develop the product vision for insitro’s industry-leading imaging software platform, and continue to support its development through method de-risking and translational development through adherence to established processes for contribution Develop methods of measuring and qualifying microscopy data prior to model training and inference, including those which leverage agentic frameworks, VLMs, and self-supervised classification Stay abreast of industrial trends and scientific literature in order to ensure insitro’s methods remain industry-leading and correctly matched to our applications. Set yearly and quarterly strategic roadmaps for platform, set tactical goals for your team, and align with cross-functional stakeholders on a regular basis Work shoulder-to-shoulder with your team where appropriate, participating in method/code review and authorship to model effective execution and cover gaps About You Strong grounding in computer vision and machine learning fundamentals, with practical judgment about where these methods work and where they fail Demonstrated experience applying CV/ML to pixel data to extract quantitative features and downstream biological insight Working understanding of imaging principles — image formation, microscopy fundamentals, and the artifacts that arise from instruments and sample preparation Hands-on experience with cellular imaging datasets (e.g. fluorescence/confocal or label-free microscopy) 3+ years of people management, including solid-line management of multiple ICs and at least one senior (Staff-level) scientist or engineer Track record of mentoring and growing technical talent day-to-day Technical leadership as a decision-maker on projects, platforms, or teams Strong communication and the ability to collaborate across functions, including with experimental and clinical life scientists