Research Scientist

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 post-training and reinforcement learning to join our ML Research team. You'll be at the forefront of developing and deploying state-of-the-art multimodal models for clinical use in radiology settings. This role owns every stage after pretraining: supervised fine-tuning, reward modeling, reinforcement learning against verifiable and learned reward signals, reasoning and tool-use training, and inference-time strategy. You'll work with one of the largest and most diverse medical imaging datasets in the industry, advancing the state-of-the-art in grounded report generation, reward design, and inference-time reasoning while maintaining the clinical rigor required for healthcare deployment.

Key Responsibilities

• Design reinforcement learning with verifiable rewards for report generation, including clinical label and entity-relation matching, grounding IoU, measurement accuracy, and reporting schema compliance.

• Extend reinforcement learning to unverifiable and noisy objectives such as report quality and clinical usefulness, using learned reward models and radiologist feedback pipelines (RLHF) built on expert preferences and report edits.

• Run GRPO-family algorithms with complex multi-reward objectives, tuning reward composition and diagnosing reward hacking, entropy collapse, and diversity loss.

• Train explicit reward models, including multimodal reward models conditioned on the image, with both outcome and process supervision.