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# Senior Machine Learning Scientist (US)

- **Company:** Altis Labs LLC
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $175,000.0 - $300,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Computer Vision, Big Data, Program Optimization, Continuous Integration, Distributed Computing Environment, Monitoring of Systems, Machine Learning, Pytorch, Deep Learning, Machine Learning Operations, Hardware Infrastructure
- **Published:** July 23, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=86f97469e77f1578

## About the Role

* 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

## Description

* 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


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