Senior Machine Learning Scientist (CA)
- Toronto, ON, Canada
$175,000 to $300,000 CAD / year · Full-time
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Job details
- Pay
- $175,000 to $300,000 CAD / year
- Job type
- Full-time
- Experience
- 7+ years of experience in machine learning, with substantial work in computer vision or medical imaging
- Posting language
- English
- Posted
Original 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
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
- 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
- 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)
- 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
- Competitive pay and generous equity participation
- Coverage for medical, vision, and dental insurance
- 4 weeks of vacation per year
More about this job
Responsibilities
Design and implement deep learning architectures for 3D volumetric medical imaging and develop survival models for time-to-event prediction. Optimize large-scale training pipelines on cloud infrastructure and contribute to research publications.
Requirements
Requires over 7 years of experience in machine learning with a focus on computer vision or medical imaging. Candidates should have deep expertise in 3D vision, survival analysis, and proficiency with PyTorch.
Skills
- Deep Learning
- Computer Vision
- Medical Imaging
- 3D Vision
- Survival Analysis
- Time-to-event Modeling
- PyTorch
- Distributed Training
- Cloud GPU Infrastructure
- 3D CNNs
- Vision Transformers
- Machine Learning
- MLOps
- Self-supervised Learning
- Uncertainty Quantification
- Statistics
Benefits
- Competitive pay
- Generous equity participation
- Medical insurance
- Vision insurance
- Dental insurance
- 4 weeks of vacation per year
Visa sponsorship
Not detected in the job text
Categories
- Data & Analytics
- Technology
- Science & Research
- Healthcare
- Software
Keywords
- AI
- Deep Learning
- Computer Vision
- Medical Imaging
- 3D CNNs
- Vision Transformers
- Survival Analysis
- Cox Models
- PyTorch
- Cloud GPU
- Distributed Training
- MLOps
- CI/CD
- Oncology
- Radiology
- CT
- PET
- MRI
- Self-supervised Learning
- Bayesian Deep Learning
- Conformal Prediction
- Biotechnology
- Biopharma