Research Engineer / Research Scientist, Post-Training & Reinforcement Learning

The role

You will own the post-training of multimodal foundation models. You will develop novel reinforcement learning methods, reward models, and reasoning capabilities for medical AI.

What you will do

• Advance the frontier of reinforcement learning.
• Develop and scale novel post-training methods for multimodal foundation models.
• Design reward functions and train multimodal reward models.
• Develop methods to improve how models reason over complex medical images.
• Train models to interact with imaging tools and reason across multi-step workflows.
• Investigate test-time compute and verifier-guided generation.
• Take ideas from hypothesis and experimentation through large-scale distributed training, evaluation, and deployment handoff.
• Develop rigorous evaluations for clinical accuracy, factuality, visual grounding, and model reliability.
• Publish original research and share technical insights.

Required qualifications

• Deep expertise in reinforcement learning, language model post-training, or multimodal machine learning.
• Demonstrated ability to develop, train, or improve state-of-the-art machine learning models.
• Strong understanding of modern post-training techniques, including reinforcement learning, reward modeling, and preference optimization.
• 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 conferences such as NeurIPS, ICML, ICLR, CVPR, or ACL.
• Experience with reinforcement learning algorithms such as GRPO, DAPO, or related policy optimization methods.
• Experience developing reward models, RLHF pipelines, or reinforcement learning with verifiable rewards.
• Familiarity with large-scale RL infrastructure, including vLLM, SGLang, verl, TRL, or OpenRLHF.
• Experience with multimodal reasoning, visual grounding, or agentic reinforcement learning.
• Experience with inference-time scaling, verifier-guided decoding, or test-time optimization.
• Background in medical imaging, radiology, or other scientific imaging applications.
• Experience evaluating model factuality, hallucinations, or reliability 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.