Senior Manager, Imaging Machine Learning

About the role

You will define and execute the machine learning strategy for extracting biological insight from high-content imaging data within Insitro's integrated discovery platform. You will own the technical direction and career development of a multi-scientist team, ensuring that computer vision and statistical methods directly support therapeutic hypothesis generation and validation. You will act as the critical bridge between wet-lab biology and computational analysis, translating biological requirements into scalable imaging pipelines. Your work will directly inform how Insitro understands disease mechanisms and evaluates target engagement in human cellular models. You will partner with software engineers to productionize methods while maintaining scientific rigor and reproducibility. You will influence the broader field by setting standards for data quality and model evaluation in cellular imaging. This role requires deep collaboration with computational biologists and laboratory scientists to close the loop between assay design and biological discovery. By aligning machine learning innovation with Insitro's phenotypic readouts, you will help drive the creation of new drug candidates.

Key facts

What you'll do

Lead the design and implementation of computer vision and machine learning methods tailored to in-vitro microscopy datasets, ensuring they address specific biological questions.

Partner with laboratory scientists to define, iterate, and validate imaging assays that support genetic perturbation screening at scale and across diverse phenotypes.

Translate biological insights from imaging experiments into structured assays that computational biology teams can integrate with omics and perturb-seq data.

Co-develop the strategic vision and roadmap for Insitro's imaging software platform, translating research prototypes into reliable, scalable tools.

Establish rigorous methods for measuring data quality and metadata integrity before model training, incorporating modern approaches to validation.

Champion the adoption of agentic frameworks, vision-language models, and self-supervised learning to improve feature extraction and inference quality.

Monitor external advances in computer vision, microscopy instrumentation, and biological machine learning to maintain Insitro's competitive edge.

Define and communicate clear technical roadmaps, milestones, and success metrics for your team in alignment with company objectives.

Engage in hands-on technical contributions, including code review, model evaluation, and co-authorship on internal or external deliverables.

Facilitate cross-functional alignment between machine learning, computational biology, and laboratory engineering through regular collaboration.

Ensure that developed methods adhere to established governance, reproducibility, and deployment standards used across Insitro's workflows.

Identify gaps in existing pipelines and propose novel solutions that integrate imaging data with other high-content and omics modalities.

Support the growth and mentorship of team members by providing feedback, career guidance, and opportunities for technical leadership.

Represent Insitro in the external scientific and machine learning community through publications, talks, and collaboration.

Requirements

Demonstrate a strong foundational understanding of computer vision and machine learning, with clear judgment on problem applicability and limitations.

Apply computer vision and machine learning techniques to pixel-level data to extract quantitative features and derive actionable biological insight.

Possess a working knowledge of image formation, microscopy principles, and common sources of artifact in biological imaging.

Have hands-on experience with cellular imaging data, including fluorescence, confocal, or label-free microscopy modalities.

Bring 3 or more years of people management experience, including managing multiple individual contributors and at least one senior-level staff scientist or engineer.

Show a track record of mentoring and developing technical talent through day-to-day interactions and performance feedback.

Operate as a technical decision-maker who leads projects, platforms, or teams with ownership and accountability.

Communicate effectively and collaborate across functions, including with experimental biologists and clinical researchers.

Work in a hybrid role with a minimum requirement to be present at the South San Francisco headquarters for three days per week.

Commit to Insitro's mission of delivering better drugs faster to patients who can benefit most through data and machine learning.

Nice to have

Experience with additional high-content modalities such as temporal phenotyping, spatial proteomics, single-cell or bulk omics, or pooled optical screening.

Familiarity with genetic perturbation screening approaches, especially at genome scale and in complex cellular models.

Hands-on experience with data-quality methods for imaging, including agentic frameworks, vision-language models, or self-supervised approaches for quality control and classification.

Conversational understanding of a relevant scientific domain outside of standard cellular imaging applications.