Principal Research Scientist, Reinforcement Learning

Centific; East Palo Alto (CA); On-site; USD 250,000 - 300,000; Full time

Job summary

Centific is seeking a senior ML/AI leader to design simulation environments and digital twins for enterprise workflows, and to post-train LLM agents using RLHF, DPO, GRPO, and PPO. You will architect multi-turn, tool-using agents and build end-to-end data pipelines for training data.

Lead the technical direction, mentor researchers, and translate research into production. Join a team of PhDs and engineers tackling high-impact GenAI challenges at scale.

Qualifications

• 7+ years in ML/AI research or engineering.
• 3+ years at senior/staff level with MS/PhD or equivalent.
• 5+ years hands-on RL with at least one production deployment.

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.

Skills

Python

ML research

RLHF

PPO

Education

MS/PhD in CS/ML

Tools

TRL

veRL

OpenRLHF

SkyRL

Gymnasium

Job description

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)

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. 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.

$250k-$300k

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. Join a growing company using technology to help tackle enterprises’ toughest challenges.