Senior Staff Research Scientist, Reinforcement Learning

About Centific

Centific is a frontier AI data foundry that curates diverse high-quality data using our purpose-built technology platforms to empower the Magnificent Seven and our enterprise clients with safe scalable AI deployment. Our team includes more than 150 PhDs and data scientists along with more than 4000 AI practitioners and engineers. We harness the power of an integrated solution ecosystemcomprising industry-leading partnerships and 1.8 million vertical domain experts in more than 230 marketsto create contextual multilingual pre-trained datasets; fine-tuned industry-specific LLMs; and RAG pipelines supported by vector databases. Our zero-distance innovation solutions for GenAI can reduce GenAI costs by up to 80% and bring solutions to market 50% faster.

Our mission is to bridge the gap between AI creators and industry leaders by bringing best practices in GenAI to unicorn innovators and enterprise customers. We aim to help these organizations unlock significant business value by deploying GenAI at scale helping to ensure they stay at the forefront of technological advancement and maintain a competitive edge in their respective markets.

About Job

• 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)

• 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

• 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.

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:

Subject: Senior Staff Research Scientist RL

$250k-$300k