Research Scientist - Post-training / RL

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