Senior Machine Learning Scientist

About 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