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Centific

Staff Research Scientist - Reinforcement Learning

Salary$200k – $250k
LocationRemote Work( USA)
Work moderemote · United States
Typefull-time
SeniorityLead
Experience7+ yrs
Company size1,001–5,000 people
PostedJul 2, 2026 · 3mo ago
Verified live6h ago
At a glanceSummarised by Seekless from the posting.
Must have19
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
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)
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
Nice to have6
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
Skills
Python
RLHF
DPO
GRPO
PPO
TRL
veRL
OpenRLHF
SkyRL
Gymnasium
NeurIPS
ICML
ICLR
ACL
COLM
CQL
IQL
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
Responsibilities
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
Requirements
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)
Nice to have
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
Benefits
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.
Salary: $200k-$250k
Hiring process
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:
[email protected]
Subject: Senior Staff Research Scientist – RL
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.
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