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 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, helping them unlock significant business value by deploying GenAI at scale to maintain a competitive edge. 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 7+ years in ML/AI research or engineering, with 3+ years at senior or 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) 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 Why Join Centific Lead the frontier and shape a new discipline at the intersection of post‑training, simulation, and enterprise AI Ship your science and see your research power real systems across healthcare, finance, and safety‑critical operations Collaborate with leaders and work alongside NVIDIA, Microsoft, and the global AI community Build governed, compliant AI systems enterprises can trust EEO Statement Centific is an equal‑opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, ancestry, citizenship status, age, mental or physical disability, medical condition, sex (including pregnancy), gender identity or expression, sexual orientation, marital status, familial status, veteran status, or any other characteristic protected by applicable law. We consider qualified applicants regardless of criminal histories, consistent with legal requirements. #J-18808-Ljbffr