We build AI models to enable smaller, faster, and more successful clinical trials.
About Altis Labs
Altis Labs is a computational imaging company focused on improving how oncology trials measure treatment benefit. Our core technology is IPRO, an AI model that generates patient-level outcome predictions directly from routine medical imaging data. Our global biopharma customers use IPRO to predict efficacy, navigate billion-dollar development decisions with confidence, and move their most promising therapies through Phase I–III trials faster. IPRO is trained on the industry’s largest real-world imaging, clinical, and outcomes database, containing over 210 million longitudinal images and more than one million patient-years of linked outcomes.
Our multidisciplinary team of AI scientists, clinicians, and business operators is on a mission to get the most effective treatments to patients sooner. We collaborate closely with academic medical centers and co-publish our results at top-tier medical conferences.
Altis is headquartered in Toronto, serves 6 of the top 20 global biopharmaceutical companies, and is backed by leading life sciences and technology investors.
What makes this role compelling:
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Unusually rich data: Access to large, diverse patient datasets with longitudinal outcomes across multiple cancer types
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Novel methodology: We’re developing approaches that push beyond standard practices in medical imaging AI
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Multi-cancer generalization: Building methods that transfer across cancer types, not one-off solutions
Responsibilities & Expectations:
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Design and implement deep learning architectures for 3D volumetric medical imaging (CT, PET, MRI)
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Develop survival models that handle censored outcomes, competing risks, and the statistical nuances of time-to-event prediction
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Optimize training pipelines to efficiently process large-scale imaging datasets on cloud GPU infrastructure
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Collaborate with our ML team to establish best practices and push the state of the art
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Contribute to research publications and present findings at conferences
Qualifications:
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7+ years of experience in machine learning, with substantial work in computer vision or medical imaging
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PhD in machine learning, computer vision, statistics, or a related field preferred; exceptional industry track record considered
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Deep expertise in 3D vision—experience with volumetric architectures (3D CNNs, Vision Transformers for 3D data, etc.)
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Strong foundation in survival analysis and time-to-event modeling (Cox models, deep survival models, competing risks)
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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
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Proficiency with PyTorch and modern ML infrastructure
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Track record of impactful research (publications, deployed systems, or equivalent demonstrations of technical depth)
Nice to have:
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Experience with medical imaging foundation models or self-supervised learning on unlabeled imaging data
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Background in uncertainty quantification: calibrated predictions, conformal prediction, Bayesian deep learning
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MLOps experience: productionizing models, CI/CD for ML, model monitoring
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Familiarity with oncology, radiology, or regulated healthcare environments
Benefits:
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Competitive pay and generous equity participation
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Coverage for medical, vision, and dental insurance