Job Description:
• 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.
Requirements:
• Doctorate degree in Computer Science, Artificial Intelligence, Machine Learning, Electrical Engineering, Computational Biology, or a related quantitative field and 3 years of related experience
• Or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Electrical Engineering, Computational Biology, or a related quantitative field and 6 years of related industry experience
• Or Bachelor’s degree in Computer Science, Artificial Intelligence, Machine Learning, Electrical Engineering, Computational Biology, or a related quantitative field and 8 years of related industry experience
• Demonstrated deep technical expertise in developing custom AI/ML models for medical imaging
• Strong experience in digital pathology and/or radiology, including whole-slide images and/or modalities such as CT, MRI, or PET
• Expertise in foundation model usage, including pre-training, fine-tuning, and domain adaptation for imaging-based tasks
• Advanced knowledge of modern computer vision and ML techniques, including: CNNs, U-Net–based architectures, Vision Transformers, Self-supervised, weakly supervised, and few-shot learning, Multimodal and representation learning approaches
• Proficiency in Python and deep learning frameworks such as PyTorch and/or TensorFlow.
• Demonstrated ability to communicate complex technical concepts clearly and influence scientific decision-making
• Strong record of scientific contributions (e.g., publications, patents, deployed models, or platform capabilities)
• Demonstrated evidence of setting and implementing technical or scientific strategies for complex AI/ML or computational imaging initiatives, including defining problem statements, selecting modeling approaches, and driving execution to measurable scientific or translational outcomes
• Strong publication record in AI/ML, with particular emphasis on applications to drug development, biomarker discovery, patient stratification, or translational research; contributions to high-impact journals or top-tier AI/medical imaging conferences strongly preferred
• Experience working with integrated imaging, clinical, and molecular datasets
• Familiarity with MLOps, scalable training, and model lifecycle management
• Experience with cloud or HPC environments (e.g., AWS, Azure, GCP, SLURM), containerization, and distributed training
• Prior experience leading significant components of cross-functional or external collaboration.
Benefits:
• A comprehensive employee benefits package, including a Retirement and Savings Plan with generous company contributions
• group medical, dental and vision coverage
• life and disability insurance
• flexible spending accounts
• A discretionary annual bonus program, or for field sales representatives, a sales-based incentive plan
• Stock-based long-term incentives
• Award-winning time-off plans
• Flexible work models where possible.