Principal Research Scientist, Reinforcement Learning

Centific • East Palo Alto, CA

3d • $250,000 - $300,000

About The Position

Centific is seeking a Principal Research Scientist specializing in Reinforcement Learning (RL) to join their team. This role involves designing simulation environments and digital twins for enterprise workflows, post-training Large Language Model (LLM) agents using various RL methods, and building data conversion pipelines. The scientist will architect multi-turn, tool-using agents with closed learning loops, design robust reward functions, and set technical standards for the team. Responsibilities also include mentoring researchers and engineers, driving technical direction, translating research into production, and contributing to publications.

Requirements

• 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
• 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)
• 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
• 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)

Nice To Haves

• 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

Responsibilities

• 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

Benefits

• Lead the frontier. Shape a new discipline at the intersection of post-training, simulation, and enterprise AI.
• Ship your science. See your research power real systems across healthcare, finance, and safety-critical operations.
• Collaborate with leaders. Work alongside NVIDIA, Microsoft, and the global AI community.
• Build what matters. Create governed, compliant AI systems enterprises can actually trust.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior