Research Engineer / Research Scientist, Vision Foundation Models

The role

You will design, pretrain, and scale vision foundation models for medical imaging including X-ray, CT, and MRI. You will explore novel learning methods and develop architectures to capture anatomical structures and abnormalities.

What you will do

• Design, pretrain, and scale vision foundation models for medical imaging across X-ray, CT, and MRI.
• Explore novel approaches to self-supervised learning, contrastive learning, masked image modeling, and joint-embedding predictive architectures.
• Develop architectures and training objectives that capture fine-grained anatomical structures, abnormalities, and clinically meaningful features.
• Extend vision foundation models to volumetric CT and MRI.
• Take ideas from hypothesis and experimentation through large-scale distributed training, evaluation, and deployment handoff.
• Develop rigorous evaluations for representation quality, generalization, transfer learning, and downstream clinical performance.
• Publish original research, share technical insights, and help shape the direction of medical AI.

Required qualifications

• Deep expertise in computer vision, visual representation learning, or large-scale vision foundation model pretraining.
• Demonstrated ability to develop, train, or improve state-of-the-art computer vision models.
• Strong understanding of modern vision architectures and self-supervised learning methods, including contrastive learning, masked image modeling, or JEPA-style objectives.
• Experience with large-scale distributed training using PyTorch or JAX.
• Ability to independently formulate research questions, design experiments, and turn promising ideas into working models.
• Strong engineering skills and a track record of implementing and extending ideas from research.
• A rigorous, experimental mindset and the ambition to solve difficult, unsolved problems.

Nice to have

• Publications at leading ML or computer vision conferences such as CVPR, ICCV, ECCV, NeurIPS, ICLR, or MICCAI.
• Experience developing or scaling vision encoders such as ViT, DINO, DINOv2, DINOv3, or similar architectures.
• Experience with 3D or spatiotemporal data, including video, volumetric imaging, or multi-view learning.
• Familiarity with CT and MRI processing, including voxel spacing, windowing, and multi-sequence imaging.
• Experience with efficient attention, long-context training, or scaling high-resolution vision models.
• Knowledge of vision-language models, multimodal learning, or cross-modal representation alignment.
• Background in medical imaging, radiology, or other scientific imaging applications.
• Experience evaluating model interpretability, robustness, or generalization in high-stakes settings.

About the company

Epsilon Health is transforming medical imaging and diagnostics by bringing frontier AI into clinical practice. The company is backed by Jack Altman, Uncork Capital, Renegade Partners, and AlleyCorp.