Job descriptionAbout Altis Labs Altis Labs is the computational imaging company accelerating clinical trials with AI. We are on a mission to help get the most effective novel treatments to patients sooner. Top 20 biopharma sponsors like AstraZeneca, Johnson & Johnson, and Bayer Pharmaceuticals use our AI models trained on the industry's largest cancer imaging database to measure treatment effect with greater confidence. Our fully-automated AI models predict efficacy from clinical trial imaging data so that sponsors can optimize trial design and accelerate development of their most promising drugs. Founded in 2019, Altis is a venture-backed AI company headquartered in Toronto. We are actively growing our team in Canada and the US across functional areas. About the Position We're looking for a Senior Machine Learning Scientist to help solve one of the hardest problems in medical AI: predicting time-to-event outcomes from high-dimensional 3D imaging data. This is technically demanding work with massive implications for the healthcare industry. What makes this role compelling: - Unusually rich data: Access to large, diverse patient datasets with longitudinal outcomes across multiple cancer types - Novel methodology: We're developing approaches that push beyond standard practices in medical imaging AI - Multi-cancer generalization: Building methods that transfer across cancer types, not one-off solutions Responsibilities & Expectations: - 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 Benefits: - Competitive pay and generous equity participation - Coverage for medical, vision, and dental insurance - 4 weeks of vacation per year