Research Scientist - Post-training / RL

Epsilon; San Francisco, Northern (CA, KY); Hybrid; USD 180,000 - 240,000; Full time

Job summary

Epsilon, a leader in medical imaging AI, seeks a Research Scientist to advance state-of-the-art post-training and reinforcement learning for clinical radiology using multimodal models. You’ll own the end-to-end pipeline from fine-tuning to inference, leveraging diverse imaging datasets and expert feedback to ground model outputs in clinical reality.

You will develop reward models and tooling to enable robust, verifiable reporting, experiment with multi-reward objectives, and publish findings to

Qualifications

• 6+ years of academia/industry experience in reinforcement learning, post-training, or multimodal ML
• Deep expertise in post-training LMs or vision-language models
• Strong foundation in GRPO/DAPO/GSPO with multi-reward objectives
• Reinforcement learning with verifiable rewards and noisy/learned signals
• Reward model training: outcome and process supervision
• Inference-time compute scaling: best-of-N and verifier-guided decoding
• Experience with multi-turn RL, tool-use trajectories, and credit assignment
• Proficiency in PyTorch or JAX, with multi-GPU/distributed training
• Experience with RL infrastructure at scale ( rollout generation ) and frameworks like verl/TRL/OpenRLHF
• Autoregressive LM work and instruction tuning
• High-quality production-grade software engineering skills

Responsibilities

• Design reinforcement learning with verifiable rewards for radiology report generation
• Extend RL to unverifiable objectives using learned reward models and radiologist feedback
• Run GRPO-family algorithms with multi-reward objectives and diagnose reward hacking
• Train explicit reward models conditioned on images with outcome and process supervision
• Train chain-of-thought reasoning over image regions with verification loops
• Develop multimodal tool use with credit assignment across multi-turn trajectories
• Create inference-time methods like best-of-N sampling and grounding-aware decoding
• Tune output stylization to institutional reporting conventions while separating content rewards
• Stay current with RL, reward modeling, and multimodal post-training research
• Publish conference work and contribute to best practices for medical VLMs at scale

Skills

Reinforcement Learning

Post-training techniques

Multimodal ML

Language-vision models

PyTorch / JAX

Distributed training

Software engineering

Healthcare AI

Education

PhD preferred in ML/AI or related field

Tools

vLLM

SGLang

verl

TRL

OpenRLHF

Job description

About Us

We're tackling one of healthcare's most critical challenges in medical imaging and diagnostics. Our company operates at the intersection of cutting-edge AI and clinical practice, building technology that directly impacts patient outcomes. We've assembled one of the industry's most comprehensive and diverse medical imaging datasets and have a proven product-market fit with a substantial customer pipeline already in place.

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.

• 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.

Qualifications

• 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, CISPO) with multi-reward objectives

  • Reinforcement learning with verifiable rewards, and with noisy, sparse, or learned reward signals

  • Reward model training: pairwise and generative reward models, outcome and process supervision

  • Preference optimization methods (DPO, IPO, ORPO, KTO) and RLHF

  • Inference-time compute scaling, including best-of-N sampling and verifier-guided decoding

• Practical experience diagnosing and mitigating reward hacking and reward over-optimization

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

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

• Experience with reinforcement learning infrastructure at scale, including rollout generation (vLLM, SGLang) and frameworks such as verl, TRL, or OpenRLHF

• Experience with autoregressive language modeling and instruction tuning

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

Preferred Qualifications

• Publications at top-tier conferences (NeurIPS, ICML, ICLR, CVPR, ACL, EMNLP, MICCAI)

• Hands-on experience with medical imaging applications, particularly radiology report generation

• Experience with agentic or multi-turn reinforcement learning, including credit assignment over tool-use trajectories

• Experience with grounded generation tasks (visual grounding, referring expression comprehension)

• Knowledge of evaluation methodologies for long-form generation, including factuality assessment and hallucination detection

• Experience mitigating catastrophic forgetting of supervised capabilities during reinforcement learning

• Familiarity with clinical NLP and medical knowledge representation

• Experience with model interpretability, explainability, and uncertainty quantification in safety-critical applications