Sr Scientist, Quantitative Bioimaging

We are seeking a Senior Scientist to advance quantitative bioimaging capabilities in support of immunology discovery research. In this role, you will work within a cross-functional team with access to multiple microscopy modalities, complex in vitro models, multimodal biological datasets, and advanced computational infrastructure.

You will develop and apply quantitative imaging, computer-vision, and machine-learning approaches to characterize cellular and tissue phenotypes, support the development of biological models, including co-cultures, organ-on-chip systems, organoids, and tissue explants, and generate insights that advance immunology discovery programs. This role combines hands-on delivery of robust, reproducible image-analysis workflows with the evaluation and application of advanced AI/ML approaches, including biomedical image segmentation, representation learning, foundation models, and scalable computational workflows. You will partner closely with wet-lab scientists, imaging specialists, computational scientists, and other subject-matter experts to translate biological questions into rigorous and impactful analysis strategies.

The role sits at the intersection of biology, bioengineering, microscopy, and computational science. Candidates with combined expertise in two or more of these areas are especially encouraged to apply.

Key Responsibilities

• Develop, optimize, and apply imaging assays and quantitative image-analysis workflows for 2D and 3D cellular and tissue models to characterize cellular and other complex biological phenotypes.
• Collaborate with wet-lab assay scientists, imaging specialists, and computational scientists from experimental and assay design through image acquisition, analysis, validation, and data delivery.
• Extract biologically meaningful, multiparametric features from high-content imaging datasets and use them to characterize disease-relevant phenotypes, mechanisms, and treatment responses.
• Apply statistical, machine-learning, and deep-learning methods to identify candidate imaging biomarkers and relate imaging phenotypes to molecular, functional, and other biological readouts.
• Contribute to the integration of imaging data with multimodal datasets, including molecular, single-cell, spatial, or perturbational data, where appropriate.
• Establish quality-control and evaluation frameworks to assess image and assay quality, distinguish biological signal from technical artifacts, and define data-quality criteria, acceptance thresholds, and analytical limitations.
• Maintain clear experimental and analytical records; analyze, visualize, and present results rigorously to both technical and nontechnical audiences.
• Communicate methods, assumptions, results, uncertainties, and limitations effectively to cross-functional project teams.
• Stay current with advances in quantitative bioimaging, image analysis, computer vision, and AI/ML for biological imaging, and evaluate opportunities to incorporate relevant approaches into team workflows.