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.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

AI/ML model developmentdeep learning architecturescomputer visionCNNsU-NetVision TransformersPythonPyTorchTensorFlowdomain adaptation

Soft Skills

communicationmentorshipcollaborationinfluenceanalytical rigorscientific credibilityinnovationproblem-solvingtechnical guidanceproject management