Senior Machine Learning Scientist (US)

Responsibilities

• Design and implement deep learning architectures for 3D volumetric medical imaging (CT, PET, MRI)
• Develop survival models that handle censored outcomes, competing risks, and the statistical nuances of time-to-event prediction
• Optimize training pipelines to efficiently process large-scale imaging datasets on cloud GPU infrastructure
• Collaborate with our ML team to establish best practices and push the state of the art
• Contribute to research publications and present findings at conferences

Qualifications

• 7+ years of experience in machine learning, with substantial work in computer vision or medical imaging
• PhD in machine learning, computer vision, statistics, or a related field preferred; exceptional industry track record considered
• Deep expertise in 3D vision—experience with volumetric architectures (3D CNNs, Vision Transformers for 3D data, etc.)
• Strong foundation in survival analysis and time-to-event modeling (Cox models, deep survival models, competing risks)
• Proven ability to train large models efficiently at scale—you understand distributed training, memory optimization, and what it takes to iterate quickly on big data
• Proficiency with PyTorch and modern ML infrastructure
• Track record of impactful research (publications, deployed systems, or equivalent demonstrations of technical depth)

Nice to Have

• Experience with medical imaging foundation models or self-supervised learning on unlabeled imaging data
• Background in uncertainty quantification: calibrated predictions, conformal prediction, Bayesian deep learning
• MLOps experience: productionizing models, CI/CD for ML, model monitoring
• Familiarity with oncology, radiology, or regulated healthcare environments