Staff Research Scientist - Reinforcement Learning

What You'll Do

• Design simulation environments and digital twins for enterprise workflows
• Post-train LLM agents using RLHF, DPO, GRPO, PPO, and emerging methods
• Build pipelines that convert human-labeled traces and verifiable signals into training data
• Architect multi-turn, tool-using agents with closed learning loops
• Design reward functions and verifiers that resist reward hacking and reflect real task outcomes
• Set the technical bar across the team - architecture, code review, engineering standards
• Mentor researchers and engineers; drive technical direction through influence
• Translate research into production; contribute to publications

Experience & Education

• 7+ years in ML/AI research or engineering; 3+ years at senior/staff level
• MS or PhD in Computer Science, Machine Learning, or related field (or equivalent)
• 5+ years hands-on RL - environment design, reward engineering, policy optimization - with at least one production deployment

LLM Post-Training

• 3+ years fine-tuning LLMs with hands-on RL post-training (RLHF, DPO, GRPO, PPO)
• Expert-level implementation of RLHF pipelines, reward modeling (Bradley-Terry), DPO, and KTO
• Working knowledge of modern post-training and rollout-serving libraries (TRL, veRL, OpenRLHF, SkyRL)

Agent Engineering

• Experience building LLM-based agents: tool use, multi-turn reasoning, trajectory evaluation
• Strong Python and software engineering skills - comfortable building production pipelines, not just notebooks

RL Foundations

• Deep expertise in MDPs, policy gradient methods (PPO, SAC), and temporal difference learning
• Hands-on experience with Gymnasium-based environments and reward engineering (sparse vs. dense)

Preferred Qualifications

• Publications at NeurIPS, ICML, ICLR, ACL, COLM, or similar venues
• Open-source contributions to post-training or agent frameworks (TRL, veRL, OpenRLHF, SkyRL)
• Experience with Offline RL (CQL, IQL), Model-based RL / World Models, or Hierarchical RL
• Background in synthetic data generation, simulation, or world models
• Domain experience in healthcare, finance, logistics, or compliance
• Distributed training on GPU clusters

How to Apply

Send your CV, a description of a technically complex system you personally built or led, and (if applicable) your publication list or open-source contributions to: