Senior Machine Learning Scientist (Ca)

What We Do
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:
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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