Senior Applied Scientist, Reinforcement Learning

About the Role

LLM post-training is where raw capability becomes reliable, safe behavior — and in healthcare, the stakes are as high as they get. You'll own the Reinforcement Learning (RL) and On-Policy Distillation (OPD) post-training pipeline end to end, to improve our models' clinical reasoning, safety, and alignment. Your models will be deployed to interact with millions of patients across diverse clinical use cases.

• Design RL and OPD post-training methods (RLHF, RLVR, OPD, etc.)

• Build and evaluate reward models, verifiers, and LLM-as-judge pipelines

• Develop conversational AI environments and simulations for healthcare RL training with synthetic data

• Automate post-training loops with agents (auto-research)

• Run rigorous experiments to understand what drives post-training gains

• Collaborate with research, engineering, and clinical teams

• MS or PhD in CS or relevant field

• 3+ years or experience in NLP, LLM training, or RL

• 1+ years experience in RL for LLM post-training

• Experience with large-scale (50B+ parameter and multi-node) LLM training

• Strong Python and PyTorch coding skills

• Experience with RLHF, RLVR, LLM-as-judge or similar methods for LLM post-training

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