Machine Learning Scientist
US$175,000 – US$300,000 / yr base salary
Posted 21 Jul 2026
Advertised as “Senior Machine Learning Scientist (US)”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 7+ yrs
- Management level
- Individual contributor
- Employment
- Remote
- Contract
- Full-time
| Base salary | US$175,000 – US$300,000 / yr |
|---|
Field of studyComputer Vision, Machine learning, Statistics (or related)
Study levelDoctorate
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:
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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