Senior Research Scientist - Vision Foundation Models

Epsilon Health; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Epsilon Health in San Francisco is seeking a Research Scientist to lead the development of vision foundation models for medical imaging. You will scale encoders for X-ray, CT, and MRI, including 3D volumetric data, ensuring clinical-grade reliability.

You will work across data curation, model design, distributed training, and production deployment to accelerate real-world impact. Join a world-class ML Research team pushing frontier AI into clinical practice, publishing results, and collaborating

Qualifications

• 6+ years of academia/industry experience in computer vision/machine learning.
• Deep expertise in training vision encoder models at scale (e.g. ViT, ConvNeXt).
• Experience training on volumetric or spatiotemporal data (video, 3D medical imaging).
• Track record of implementing complex models from research papers and adapting them to new domains
• Proficiency in PyTorch or JAX, with experience training models on multi-GPU/distributed systems
• Hands-on experience with medical imaging applications, particularly radiology (X-ray, CT, MRI)
• Strong software engineering skills and ability to write production-quality code

Responsibilities

• Design, train, and scale vision foundation models for radiology applications across X-ray, CT, and MRI modalities, implementing self-supervised, contrastive, masked image modeling, and JEPA frameworks.
• Extend 2D pretraining recipes to volumetric CT and MR data, addressing long sequence lengths and multi-sequence studies.
• Evaluate model performance rigorously across academic benchmarks, internal offline datasets, and live production data.
• Contribute hands-on to dataset curation, architecture design, distributed training, and production deployment.
• Stay current with cutting-edge research in computer vision and medical imaging AI.
• Drive research and technical excellence through conference publications and technical blog posts, establishing best practices for training robust medical imaging models at scale.

Skills

Vision foundation models

Self-supervised pretraining

Volumetric data

Multi-GPU training

PyTorch

JAX

Medical imaging

Tools

PyTorch

JAX

DICOM basics

Job description

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 will 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.

Role Overview

We’re seeking a Research Scientist with deep expertise in vision foundation models to join our ML Research team. You’ll be at the forefront of developing and deploying state-of-the-art vision models for medical imaging applications. This role focuses on pretraining and scaling vision encoders for radiology diagnosis across X-ray, CT, and MRI, with a growing emphasis on 3D volumetric modeling. You’ll work with one of the largest and most diverse medical imaging datasets in the industry, pushing the boundaries of what’s possible in AI-assisted diagnosis while maintaining the rigor required for clinical deployment.

Key Responsibilities

• Design, train, and scale vision foundation models for radiology applications across X-ray, CT, and MRI modalities, implementing self-supervised, contrastive, masked image modeling, and joint-embedding predictive (JEPA) frameworks.

• Extend 2D pretraining recipes to volumetric CT and MR data, addressing long sequence lengths, anisotropic spacing, and multi-sequence studies.

• Evaluate model performance rigorously across academic benchmarks, internal offline datasets, and live production data.

• Contribute hands-on to all stages of model development including dataset curation, architecture design, distributed training, and production deployment.

• Stay current with cutting-edge research in computer vision and medical imaging AI.

• Drive research and technical excellence through conference publications and technical blog posts, establishing best practices for training robust medical imaging models at scale.

Qualifications

• 6+ years of academia/industry experience in computer vision/machine learning

• Deep expertise in training vision encoder models at scale (e.g. ViT, ConvNeXt). Strong foundation in self-supervised pretraining, including contrastive, masked image modeling, self-distillation, and JEPA-style objectives.

• Experience training on volumetric or spatiotemporal data (video, 3D medical imaging)

• Track record of implementing complex models from research papers and adapting them to new domains

• Proficiency in PyTorch or JAX, with experience training models on multi-GPU/distributed systems

• Hands-on experience with medical imaging applications, particularly radiology (X-ray, CT, MRI)

• Strong software engineering skills and ability to write production-quality code

Preferred Qualifications

• Publications at top-tier conferences (CVPR, ICCV/ECCV, NeurIPS, ICLR, MICCAI)

• Experience with 3D medical image processing and retrieval tasks

• Familiarity with CT and MR acquisition (windowing, multi-sequence protocols, voxel spacing)

• Experience with long-context training techniques (sequence parallelism, efficient attention)

• Knowledge of vision-language models and multimodal learning

• Experience with model interpretability and explainability methods

• Understanding of clinical evaluation metrics, clinical workflows, and healthcare data (DICOM, HL7, etc.)