• Lead the design, development, training, and validation of AI/ML models for digital pathology and/or radiology applications • Define technical direction for custom AI/ML model development, including architecture selection, training paradigms, validation strategies, and performance benchmarks • Develop custom deep learning architectures and workflows for segmentation, classification, representation learning, and prediction tasks • Leverage and adapt foundation models (e.g., vision transformers, multimodal and self-supervised models), including fine-tuning and domain adaptation using proprietary datasets • Extract insights from large-scale imaging datasets, including whole-slide images and radiology modalities (CT, MRI, PET) • Apply advanced computer vision and machine learning methods, including CNNs, U-Net variants, Vision Transformers, diffusion-based or representation-learning models • Define appropriate evaluation strategies and ensure analytical rigor, reproducibility, and scientific credibility • Integrate imaging data with clinical, molecular, or spatial-omics data where relevant • Balance innovation with practicality, ensuring solutions are scalable, interpretable, and fit-for-purpose • Work closely with pathologists, radiologists, clinicians, and translational scientists to translate scientific questions into computational imaging solutions • Clearly communicate modeling approaches, assumptions, results, and limitations to technical and non-technical stakeholders • Contribute to shaping project-level research questions and study designs involving imaging data • Support external collaborations through technical input and scientific exchange as needed • Contribute to the organization’s scientific visibility through publications, presentations, and internal knowledge sharing • Provide informal mentorship and technical guidance to junior scientists and collaborators • Stay current with advances in AI, computer vision, and medical imaging to continuously elevate technical approaches.