Imaging AI Scientist

Responsibilities

• Build reusable tools, pipelines, and agent-callable workflows that scale imaging methods and multiply what partner teams can deliver.
• Develop, evaluate, and apply ML methods that produce reproducible, interpretable, and/or actionable imaging-derived insights.
• Translate biological, translational, and clinical questions into fit-for-purpose imaging and analysis strategies.
• Advance new imaging models and methods where they strengthen the science.
• Partner across biology, translational, clinical, and computational teams to understand the questions behind therapeutic programs and put solutions into practice.
• Contribute to multimodal approaches that integrate imaging with biological, clinical, or translational evidence.

Requirements

• Demonstrated excellence with current AI/ML technology, turning ambiguous problems into effective, modern solutions that work in practice.
• Experience translating analytical or ML methods into reusable code, tools, or pipelines adopted by others beyond the original author.
• Track record delivering models or analyses that perform in real-world settings, with strong intuition for data quality, labeling, evaluation, and reproducibility.
• Proficiency in Python and modern ML frameworks (e.g., PyTorch), with clean, maintainable, reusable code.
• Demonstrated ability to ramp into and operate with discipline in a rigorous, high-stakes domain.
• Ability to work at the interface of imaging science, ML, and engineering, and drive work to real-world impact.

Preferred

• Hands-on experience with clinical imaging data (e.g., MRI, CT, PET, OCT) or tissue-based imaging (e.g., digital pathology, spatial transcriptomics, spatial proteomics).
• Experience developing quantitative imaging strategies for clinical studies, such as endpoint selection, analysis plans, and data quality.
• Experience developing or integrating AI-enabled automation, agentic workflows, or decision-support tools for scientific analysis.
• Experience building ML or computational workflows, including data pipelines, evaluation frameworks, deployment patterns, monitoring, or MLOps practices.
• Experience with advanced ML such as multimodal modeling, representation learning, generative modeling, or interpretability, particularly in applied or regulated settings.
• Familiarity with regulatory or validation considerations for clinical applications, such as biomarker validation, fit-for-purpose evaluation, or GxP practices.

Pay

The expected salary range for this position based on the primary location of California is $124,800.00 - $231,800.00 USD annually. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law.

Schedule

N/A

Benefits

This position also qualifies for the benefits detailed at the link provided below.