Senior Machine Learning Scientist
4 days ago
About Altis LabsAltis 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 PositionWe'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 typesNovel methodology: We're developing approaches that push beyond standard practices in medical imaging AIMulti-cancer generalization: Building methods that transfer across cancer types, not one-off solutionsResponsibilities & 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 predictionOptimize training pipelines to efficiently process large-scale imaging datasets on cloud GPU infrastructureCollaborate with our ML team to establish best practices and push the state of the artContribute to research publications and present findings at conferencesQualifications:7+ years of experience in machine learning, with substantial work in computer vision or medical imagingPhD in machine learning, computer vision, statistics, or a related field preferred; exceptional industry track record consideredDeep 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 dataProficiency with PyTorch and modern ML infrastructureTrack 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 dataBackground in uncertainty quantification: calibrated predictions, conformal prediction, Bayesian deep learningMLOps experience: productionizing models, CI/CD for ML, model monitoringFamiliarity with oncology, radiology, or regulated healthcare environmentsBenefits:Competitive pay and generous equity participationCoverage for medical, vision, and dental insurance4 weeks of vacation per year
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