Principal Research Scientist, Reinforcement Learning
Centific Open · verified Oct 22, 2026 At a glance
Senior AI Research role at Centific. East Palo Alto · full-time · $250,000–$300,000 base.
aimlroles summary, based on the employer's posting.
What you'll do
- Build simulated settings and digital replicas that model enterprise workflows
- Train language-model agents after pretraining with RLHF, DPO, GRPO, PPO and newer approaches
- Turn annotated interaction records and checkable signals into data for training
- Create multi-step agents that use tools and improve through closed feedback loops
- Set design and code standards, mentor technical staff, and bring research into deployed systems and publications
What you bring
- At least seven years in AI or ML research or engineering, including three at senior or staff level
- A master's or doctoral degree in computer science, machine learning or a related area, or equivalent experience
- At least five years applying reinforcement learning, including environment and reward design, policy optimization and a production launch
- Three or more years tuning LLMs with RL; expert implementation of RLHF, reward models, DPO and KTO
- Strong Python and production engineering skills, with experience building tool-using agents that reason across multiple turns
Who this fits
This senior role suits a hands-on research leader who can guide technical direction, mentor others and move systems into production. The work combines reinforcement learning, agent development and research delivery. The role is located in East Palo Alto.
From the employer
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.
How to Apply
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:
diana.moeck@centific.com
Subject: Senior Staff Research Scientist – RL
$250k-$300k +