Senior Machine Learning Scientist (CA)

Altis Labs, Inc.; Toronto; On-site; CAD 180,000 - 280,000; Full time

Benefits offered by this job

Equity

Medical, vision, dental

4 weeks vacation

Job summary

Altis Labs in Toronto seeks a senior ML scientist focused on 3D medical imaging and survival analysis to advance IPRO models for oncology trials. You will design deep learning architectures on CT/PET/MRI data and scale training on cloud GPUs.

Collaborate with ML and clinical teams, publish findings, and contribute to product-ready solutions with robust uncertainty handling and deployment considerations.

Qualifications

• 7+ years of ML experience with a focus on medical imaging or CV.
• PhD preferred; exceptional industry track record considered.
• Deep expertise in 3D vision and survival modeling.
• Experience with distributed training and scalable ML infra.

Responsibilities

• Design and implement 3D deep learning architectures for medical imaging.
• Develop survival models handling censored data and competing risks.
• Scale training pipelines on cloud GPU infrastructure.
• Collaborate with ML and clinical teams; publish findings.

Skills

3D vision

Survival analysis

PyTorch

Distributed training

Education

PhD in ML / CV / Stats

Tools

PyTorch

Cloud GPUs

Job description

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 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