About Us

Epsilon Health is transforming medical imaging and diagnostics by bringing frontier AI into clinical practice. We're tackling some of healthcare's most critical challenges, building technology that directly improves patient outcomes.

This year, we've served over 300,000 patients and now serve 3,000+ daily, working with some of the largest medical institutions in the U.S. By year-end, we're on track to interpret 1% of all daily U.S. X-rays.

Our research team brings experience from DeepMind and Meta, powered by one of the industry's most comprehensive proprietary medical imaging datasets, continuously enriched by real-world clinical data. Together, we're building a future where frontier AI helps save millions of lives today, not tomorrow.

Recently out of stealth, we're proudly backed by Jack Altman, Uncork Capital, Renegade Partners, and AlleyCorp, one of NYC's most active healthcare venture firms.

About the Role

We're looking for an exceptional Research Engineer or Research Scientist to push the frontier of vision foundation models for medical imaging.

You'll own the development, pretraining, and scaling of our vision encoders, building models capable of understanding complex medical images across X-rays, CT, and MRI.

You'll tackle fundamental research challenges in self-supervised learning, visual representation learning, large-scale pretraining, and 3D volumetric modeling.

Unlike traditional research labs, your work will draw on continuously growing real-world clinical data and help power models deployed into clinical practice, creating a direct feedback loop between research and patient care.

You'll work alongside a small team of researchers and engineers, with the freedom to pursue ambitious ideas and the resources to bring them to life. You'll collaborate closely with researchers developing our multimodal foundation models.

What You'll Do

• Advance the frontier of computer vision. Design, pretrain, and scale vision foundation models for medical imaging across X-ray, CT, and MRI.

• Invent new learning methods. Explore novel approaches to self-supervised learning, contrastive learning, masked image modeling, and joint-embedding predictive architectures.

• Push the limits of visual representation learning. Develop architectures and training objectives that capture fine-grained anatomical structures, abnormalities, and clinically meaningful features.

• Solve 3D visual understanding. Extend vision foundation models to volumetric CT and MRI, addressing challenges in spatial reasoning, long sequences, and multi-sequence imaging.

• Own research end to end. Take ideas from hypothesis and experimentation through large-scale distributed training, evaluation, and deployment handoff.

• Define how progress is measured. Develop rigorous evaluations for representation quality, generalization, transfer learning, and downstream clinical performance.

• Contribute to the research community. Publish original research, share technical insights, and help shape the direction of medical AI.

What We're Looking For

We care more about what you've built and discovered than where you've worked or how many years you've spent doing it.

• 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.

We welcome exceptional researchers from both academic and industry backgrounds. A traditional career path or prior healthcare experience is not required.