Senior Machine Learning Scientist (CA)

Benchstrength; Toronto; Hybrid; CAD 175,000 - 300,000; Full time

Benefits offered by this job

Competitive pay

Equity participation

Medical, vision, dental insurance

4 weeks vacation

Job summary

Altis Labs, a Toronto-based computational imaging company, seeks a senior ML researcher to design and implement 3D medical imaging models and survival analysis methods. You will work on IPRO, contributing to cutting-edge research and collaborations with biopharma partners.

The role emphasizes large-scale model training, cloud GPU pipelines, and publications, with competitive compensation and equity. This on-site Toronto position supports rapid iteration and impact across cancer trials.

Qualifications

• PhD in ML/CS/Statistics or related field; track record of research excellence.
• Extensive experience with 3D medical imaging and survival analysis.
• Experience training large models at scale and deploying on cloud GPUs.
• Strong publication or deployment record.

Responsibilities

• Design deep learning architectures for 3D medical imaging (CT, PET, MRI).
• Develop survival models handling censored data and competing risks.
• Optimize training pipelines for large-scale imaging data on cloud GPUs.
• Collaborate with ML team to advance best practices and publish results.
• Contribute to research publications and conference presentations.

Skills

3D vision expertise

Survival analysis

Time-to-event modeling

Large-scale training

Research communication

Education

PhD in ML/CS/Statistics

Tools

PyTorch

Cloud GPU infrastructure

3D CNNs

Vision Transformers (3D)

Distributed training

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

Compensation Range: CA$175K - CA$300K