Principal Scientist, Imaging Analytics

Role Overview

• Lead end-to-end AI applications on trial imaging data (CT, MRI, PET) for quantitative imaging measures and AI-derived endpoints
• Collaborate internally and externally to drive scientific innovation in foundational imaging AI that are relevant to oncology drug development — including automated segmentation, radiomics, and multimodal predictive modeling through hands-on research
• Translate imaging-derived evidence into actionable insights by converting complex quantitative findings into clear scientific narratives and engaging cross-functional stakeholders
• Provide scientific leadership to external partnerships — including imaging AI vendors, CROs, biomarker companies, academic centers, and imaging OEMs — to accelerate model development, validation, and deployment
• Publish and present scientific innovation at top scientific and clinical conferences (e.g., MICCAI, AACR, RSNA, etc.)

Requirements

• Ph.D. in Computer Science, Biomedical Engineering, Electrical Engineering, or a related quantitative field
• 3+ years of post-doctoral or industry experience developing AI/ML for medical imaging (CT, MRI, PET) in a clinical setting
• Hands-on expertise across the medical imaging AI stack: deep learning (segmentation, detection, classification, registration), radiomics, and multimodal predictive modeling
• Proficiency in Python and PyTorch, with practical experience in medical-imaging libraries such as MONAI, SimpleITK, ITK, PyRadiomics, nnU-Net, 3D Slicer, and OpenCV
• Experience with cloud ML infrastructure and MLOps practices for scalable training and inference on imaging data
• Extensive experience with the full imaging data workflow: DICOM I/O, visualization, registration, harmonization, annotation, and segmentation of 3D medical images
• Strong peer-reviewed publication record and demonstrated ability to communicate complex scientific concepts to both technical and cross-functional audiences
• Preferred: Experience in analyzing solid-tumor imaging, particularly lung and head & neck (H&N)
• Track record of developing and applying AI/ML to oncology imaging and within oncology clinical trials
• Familiarity with standard oncology endpoints (e.g., RECIST 1.1, iRECIST, PERCIST)
• Experience building and scaling clinical or imaging AI platforms end-to-end, including data ingestion, harmonization, model inference, visualization, and continuous monitoring
• Experience in sourcing, structuring, and managing external partnerships with imaging-AI vendors, CROs / imaging core labs, biomarker companies, and academic centers.

Tech Stack

• Cloud
• Python
• PyTorch

Benefits

• Consolidated retirement plan (pension)
• Savings plan (401(k))
• Vacation –120 hours per calendar year
• Sick time
• 40 hours per calendar year; for employees who reside in Colorado –48 hours per calendar year; for employees in Washington –56 hours per calendar year
• Holiday pay, including Floating Holidays –13 days per calendar year
• Work, Personal and Family Time
• up to 40 hours per calendar year
• Parental Leave – 480 hours within one year of the birth/adoption/foster care of a child
• Bereavement Leave – 240 hours for an immediate family member: 40 hours for an extended family member per calendar year
• Caregiver Leave – 80 hours in a 52-week rolling period
• Volunteer Leave – 32 hours per calendar year
• Military Spouse Time-Off – 80 hours per calendar year