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Rlhf Jobs (NOW HIRING)

Agentic AI Engineer Lead

Dallas, TX · On-site

$101K - $133K/yr

The ideal candidate will have deep expertise in LLM orchestration, knowledge graphs, reinforcement learning (RLHF/RLAIF), and real-world AI applications. As a leader in this space, they will be ...

Showing results 41-60

Rlhf information

What is an RLHF job?

An RLHF (Reinforcement Learning with Human Feedback) job involves training AI models using human feedback to improve their responses. Professionals in this role analyze model outputs, provide evaluations, and refine AI behavior through reinforcement learning techniques. These roles are common in AI research, content moderation, and chatbot development.

What are the key skills and qualifications needed to thrive as a Reinforcement Learning from Human Feedback (RLHF) engineer, and why are they important?

To thrive as an RLHF Engineer, you need a strong background in machine learning, reinforcement learning, and programming (often Python), typically supported by an advanced degree in computer science or a related field. Experience with ML frameworks (such as TensorFlow or PyTorch), data annotation tools, and familiarity with large language models are typically required. Strong analytical thinking, collaboration, and clear communication are essential soft skills to succeed in research-driven, interdisciplinary teams. These skills and qualities are crucial for developing safe, effective AI systems that integrate human feedback and adapt to complex real-world tasks.

What are some common challenges faced by professionals working in Reinforcement Learning from Human Feedback (RLHF) roles?

Professionals in RLHF roles often encounter challenges related to data quality and alignment between human feedback and model behavior. Collecting consistent, unbiased feedback from human annotators can be complex, and ensuring that the reinforcement learning model interprets this feedback correctly requires careful design of reward functions and training protocols. Additionally, balancing the need for rapid experimentation with maintaining rigorous evaluation standards is crucial. Collaboration with interdisciplinary teams, including data scientists, ML engineers, and domain experts, is common to address these challenges and improve model alignment.

What is the difference between Rlhf vs Rn?

AspectRlhfRn
Required CredentialsLicensed healthcare professional, often with specialized training in mental health or behavioral healthLicensed practical nurse or registered nurse, with nursing licensure and possibly additional certifications
Work EnvironmentBehavioral health facilities, clinics, hospitals, or community health settingsHospitals, clinics, long-term care facilities, and community health settings
Employer & Industry UsageBehavioral health and mental health servicesGeneral healthcare and nursing services
Common Search & ComparisonRlhf vs RnRlhf vs Rn

While Rlhf (Registered Licensed Mental Health Facilitator) focuses on mental health support and behavioral health interventions, Rn (Registered Nurse) provides broader nursing care across various medical settings. Both roles require licensure, but Rlhf specializes in mental health, whereas Rn covers general patient care.

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What cities are hiring for Rlhf jobs?

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What are the most commonly searched types of Rlhf jobs?

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What states have the most Rlhf jobs?

States with the most job openings for Rlhf jobs include:

Infographic showing various Rlhf job openings in the United States as of August 2026, with employment types broken down into 70% Full Time, 6% Part Time, and 24% Contract. Highlights an 59% In-person, and 41% Remote job distribution.

Senior Staff Research Scientist, Reinforcement Learning

Centific Global Solutions, Inc.

East Palo Alto, CA • On-site

$130 - $180/hr

Other

Posted 2 days ago

New


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, 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.

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