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Insitro

ML-driven predictive drug discovery and development

Senior Manager - Imaging Machine Learning

Computer VisionConfirmed live in the last 24 hours
Full-timeSouth San Francisco, CA, USA

About the Job

Requirements

  • Strong grounding in computer vision and machine learning fundamentals, with practical judgment about where these methods work and where they fail.
  • Demonstrated experience applying computer vision and machine learning to pixel data to extract quantitative features and downstream biological insight.
  • Working understanding of imaging principles, including image formation, microscopy fundamentals, and artifacts arising from instruments and sample preparation.
  • Hands-on experience with cellular imaging datasets, such as fluorescence/confocal or label-free microscopy.
  • At least three years of people management, including solid-line management of multiple individual contributors 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.
  • Ability to collaborate across functions, including with experimental and clinical life scientists.

Responsibilities

  • Lead, mentor, and grow a team of machine learning scientists to co-develop methods for extracting insights from in-vitro microscopy datasets.
  • Work with laboratory leadership to structure assay development efforts and empower team members to co-develop in-vitro microscopy assays suitable for genetic perturbation screening, often at whole-genome scale.
  • Ensure successful translation of developed assays to the computational biology team for investigating causal biology through integration of diverse data modalities, including microscopy.
  • Co-develop the product vision for the imaging software platform and support its development through method de-risking and translational development in accordance with established contribution processes.
  • Develop methods to measure and qualify microscopy data before model training and inference, including methods leveraging agentic frameworks, vision-language models, and self-supervised classification.
  • Monitor industrial trends and scientific literature to ensure methods remain current and appropriate to applications.
  • Set yearly and quarterly strategic roadmaps for the platform, set tactical goals for the team, and align regularly with cross-functional stakeholders.
  • Participate in method and code reviews and authorship alongside the team where appropriate, modeling effective execution and covering gaps.

Desired Qualifications

  • Experience across additional high-content modalities, including temporal phenotyping, spatial proteomics, single-cell or bulk omics, or pooled optical (in-situ) screening.
  • Familiarity with genetic perturbation screening, ideally at genome scale.
  • Experience with imaging data-quality methods, including agentic frameworks, vision-language models, or self-supervised approaches for quality control and classification.
  • Conversational understanding of a relevant disease area, such as neuroscience, metabolic disease, or cancer biology.
  • Experience contributing to or shaping an imaging or computer vision software platform.

About the Company

Insitro

ML-driven predictive drug discovery and development

Insitro integrates machine learning with biology to speed up drug discovery and development. It builds predictive models from large-scale data to forecast which targets, molecules, or paths are most likely to succeed and prioritizes experiments accordingly. Unlike firms that rely on either biology or ML alone, it combines data generation and modeling in an integrated workflow with scientists and engineers collaborating on drug candidates. Its goal is to shorten development timelines and reduce costly failures by delivering data-driven insights and co-developing medicines with pharma partners.

Company size
201-500
Company stage
Series C
Total funding
$643M
Headquarters
San Francisco, CA
Founded
2018

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