Senior Research Scientist - Post-training​/RL

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 Deep Mind 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 Alley Corp, one of NYC's most active healthcare venture firms.

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.

• 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.

• Train chain-of-thought reasoning over image regions, including evidence localization and verification loops that keep reasoning grounded in the image rather than in language priors.

• Train multimodal tool use — windowing, zoom and crop, detector and segmentation calls, prior study retrieval — with credit assignment across multi-turn trajectories.

• Develop inference-time methods including best-of-N sampling against reward models and grounding-aware decoding, and distill the resulting gains back into the policy.

• Tune output stylization to institutional reporting conventions, keeping style rewards separated from clinical content rewards.

• Stay current with cutting-edge research in reinforcement learning, reward modeling, and multimodal post-training.

• Drive research and technical excellence through conference publications and technical blog posts, establishing best practices for post‑training medical VLMs at scale.

• 6+ years of academia/industry experience in reinforcement learning, post‑training, or multimodal machine learning

• Deep expertise in post‑training large language or vision‑language models (e.g., Qwen‑VL, InternVL, LLaVA, or similar architectures)

• Strong foundation in modern post‑training and reinforcement learning techniques including:

  • Group‑relative policy optimization and its successors (GRPO, DAPO, GSPO,…