Publié il y a 4 mois
Senior Machine Learning Scientist (CA)
@ Altis Labs
Toronto, Ontario
En bref
Altis Labs is hiring a Senior Machine Learning Scientist to build deep learning models for 3D medical imaging and time-to-event prediction in oncology trials. The role involves designing volumetric architectures for CT, PET and MRI, developing survival models, and scaling training on cloud GPUs. The posting states full-time employment, competitive pay with equity, and medical, vision and dental coverage.
Salaire :
175 000 $–300 000 $ par année
Responsabilités :
- Design deep learning architectures for 3D volumetric medical imaging
- Develop survival models for censored outcomes and competing risks
- Optimize training pipelines on cloud GPU infrastructure
- Collaborate with the ML team on best practices
- Contribute to research publications and conference presentations
Résumé des exigences :
- 7+ years of machine learning experience, substantial in computer vision or medical imaging
- Deep expertise in 3D vision and volumetric architectures
- Strong foundation in survival analysis and time-to-event modeling
- Proven ability to train large models efficiently at scale
- Proficiency with PyTorch and modern ML infrastructure
- PhD preferred; exceptional industry track record considered
Outils et compétences
Avantages mentionnés
Source et dates
Site carrière / système de candidatures · ashby
- Date publiée par l’employeur
- 2026-07-21
- Première détection par notre source
- 2026-10-09
- Mise à jour du dossier source
- 2026-10-09
Ces dates décrivent le dossier source, pas une vérification en direct du recrutement. Consultez l’offre originale.
Détails du poste
| Niveau | Senior |
| Domaine | Développement logiciel |
| Expérience | 7 an(s) et plus |
| Heures par semaine | 40 (estimé) |
| Milieu de travail | Bureau |
| Position | Surtout assis |
| Exigences physiques | physique faible · ordinateur élevé |
| Langues | English |
| Formation | Doctorat (préféré) |
| Domaines d’études | machine learning, computer vision, statistics |
| Avantages mentionnés | Congés payés généreux, Actions ou participation |
Résumé et détails rédigés par l’IA à partir de l’offre; l’offre de l’employeur fait foi.
Altis Labs
Profil d’entreprise sauvegardé
Renseignements historiques provenant d’un profil The Jobs API sauvegardé (champs LinkedIn/Crunchbase). Ils ne sont pas vérifiés en temps réel; les chiffres peuvent avoir changé. Les valeurs manquantes restent inconnues.
Enabling smaller, faster, and more successful clinical trials with AI.
Description complète de l’entreprise
Altis Labs is the computational imaging company accelerating clinical trials with AI. Our multi-disciplinary team is on a mission to help reduce the time, cost, and failure rate plaguing therapeutic development so that patients can live longer, healthier lives. Trained on the industry's largest cancer imaging database linked to longitudinal clinical context, our imaging-based prognostication (IPRO) models have learned to predict patient outcomes beyond simplistic tumor size measurements. Leading global biopharmaceutical companies and cancer centers analyze their clinical trial data with IPRO to optimize trial design and get their most effective novel treatments to patients sooner. Please email [email protected] to learn more.
- Abonnés LinkedIn
- 16 165
- Personnes associées sur LinkedIn
- 29
- Bureaux déclarés
- 661 University Ave, Suite 415, MaRS Centre, West Tower, Toronto, Ontario M5G 1M1, CA
- Catégories d’activité
- Artificial Intelligence (AI)
- Bioinformatics
- Biopharma
- Clinical Trials
- Cloud Computing
- Enterprise Software
- Generative AI
- Health Care
- Health Diagnostics
- Life Science
- Machine Learning
- Pharmaceutical
- Predictive Analytics
- Software
- Autres noms enregistrés
- Altis Labs
- Spécialités
- Imaging Biomarkers
- Oncology
- Clinical Trials
- Radiomics
- Clinical Research
- Healthcare
- Cancer Research
- Machine Learning
- Computer Vision
- Deep Learning
- Radiogenomics
- Pharmacovigilance
- Precision Medicine
- Real-World Evidence
- Real-World Data
- Biotechnology
- Software
- Medicine
- Personalized Medicine
- Artificial Intelligence
- Bioinformatics
- Translational Research
- Computational Medicine
- and Digital Twins
- Le profil indique une agence de recrutement
- Non
- Investissement total déclaré
- 6 002 574
La devise et la date d’observation ne sont pas fournies; ce n’est ni une valorisation actuelle ni une ronde de financement.
Altis Labs is the computational imaging company accelerating clinical trials with AI. Our multi-disciplinary team is on a mission to help reduce the time, cost, and failure rate plaguing therapeutic development so that patients can live longer, healthier lives. Trained on the industry's largest cancer imaging database linked to longitudinal clinical context, our imaging-based prognostication (IPRO) models have…
| Champ | Valeur |
|---|---|
| Secteur | Biotechnology Research |
| Taille | 11-50 employees |
| Siège social | Toronto, Ontario |
| Type d’organisation | Privately Held |
| Fondée en | 2019 |
| Spécialités | Imaging BiomarkersOncologyClinical TrialsRadiomicsClinical ResearchHealthcareCancer ResearchMachine LearningComputer VisionDeep Learning |
| Offres sur Jobsy | 3 |
| Voir la page |
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
Journal
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