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 4,000 AI practitioners and engineers. We harness the power of an integrated solution ecosystem—comprising industry-leading partnerships and 1.8 million vertical domain experts in more than 230 markets—to 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

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 4,000 AI practitioners and engineers. We harness the power of an integrated solution ecosystem—comprising industry-leading partnerships and 1.8 million vertical domain experts in more than 230 markets—to 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

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

Required Qualifications

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)

P