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How much do reinforcement learning human feedback jobs pay per hour?

As of Sep 10, 2026, the average hourly pay for reinforcement learning human feedback in the United States is $22.78, according to ZipRecruiter salary data. Most workers in this role earn between $19.23 and $25.00 per hour, depending on experience, location, and employer.

What is reinforcement learning from human feedback?

Reinforcement Learning from Human Feedback (RLHF) is a machine learning technique where models are trained not just on data, but also on feedback provided by humans. In RLHF, humans evaluate or rank the outputs of AI systems, and this feedback is used to guide the AI's learning process. This approach helps create AI systems that better align with human values, preferences, and expectations by incorporating human judgment into the training loop. RLHF is commonly used in developing conversational AI and large language models to ensure their responses are helpful, safe, and relevant.

How does a reinforcement learning human feedback specialist typically collaborate with cross-functional teams during model development?

As an RLHF specialist, collaboration with cross-functional teams—such as software engineers, data scientists, and product managers—is essential throughout the model development lifecycle. You’ll work closely with engineers to integrate human feedback mechanisms into machine learning pipelines, and with data scientists to design effective reward models and evaluate model outputs. Regular communication ensures that the human feedback is accurately incorporated and aligns with product goals, and you may also coordinate user studies or annotation processes with UX researchers. This interdisciplinary teamwork helps ensure the RLHF process produces robust, user-aligned AI systems.

What are the key skills and qualifications needed to thrive as a reinforcement learning human feedback engineer, and why are they important?

To thrive as a Reinforcement Learning Human Feedback (RLHF) Engineer, you need a solid background in machine learning, reinforcement learning algorithms, and data analysis, typically supported by a degree in computer science or a related field. Familiarity with Python, deep learning frameworks (such as TensorFlow or PyTorch), and tools for data labeling and annotation is essential. Strong problem-solving abilities, attention to detail, and effective collaboration skills help you interpret human feedback and refine AI models. These competencies are crucial for developing robust AI systems that align with human values and real-world expectations.

What is the difference between Reinforcement Learning Human Feedback vs Reinforcement Learning Engineer?

AspectReinforcement Learning Human FeedbackReinforcement Learning Engineer
CredentialsKnowledge of machine learning, data analysis, and user feedback integrationStrong programming skills, machine learning, and software engineering background
Work EnvironmentResearch labs, AI development teams, data collection settingsSoftware development teams, AI product deployment environments
Industry UsageAI research, product refinement, user experience optimizationAI system development, algorithm implementation, model deployment

Reinforcement Learning Human Feedback focuses on collecting and analyzing human input to improve AI models, often involving data collection and feedback mechanisms. Reinforcement Learning Engineers design, implement, and optimize algorithms and systems that enable AI agents to learn from interactions. While both roles work within AI and machine learning, the former emphasizes human-in-the-loop data collection, whereas the latter concentrates on system development and deployment.

What is reinforcement learning for human feedback?

Reinforcement learning for human feedback is a method where algorithms learn to make decisions based on input and preferences provided by humans. It involves collecting human evaluations to guide the model's training, often improving performance in complex tasks. This approach is used in developing AI systems that align with human values and expectations.

What other helpful pages are available for Reinforcement Learning Human Feedback?

Other pages related to Reinforcement Learning Human Feedback:

Infographic showing various Reinforcement Learning Human Feedback job openings in the United States as of September 2026, with employment types broken down into 72% Full Time, and 28% Part Time. Highlights an 94% In-person, and 6% Remote job distribution, with an average salary of $47,374 per year, or $22.8 per hour.

Reinforcement Learning Engineer - Whole Body Control

San Jose, CA • Hybrid

Figure
See more jobs at Figureexternal

$150K/yr

Full-time

Medical

Re-posted 4 days ago

Job description

Figure is an AI Robotics company autonomous general-purpose humanoid robots. The goal of the company is to ship humanoid robots with human level intelligence. Its robots are engineered to perform a variety of tasks in the home and commercial markets. We are based in North San Jose, CA and require 5 days/week in-office collaboration. It's time to build.

We are looking for a Reinforcement Learning Engineer to develop, train, deploy, and evaluate advanced reinforcement learning algorithms for whole body control of our humanoid robot.

Key Responsibilities:

  • Develop, train, and deploy reinforcement learning algorithms for whole body control
  • Determine the observations, actions, and model types that unlock maximum performance
  • Identify and close the most important sim-to-real gaps
  • Define, test, and evaluate performance metrics for learned policies
  • Harden the control stack to ensure rock solid robustness

Requirements:

  • Strong background in dynamics and control, ideally of legged robots
  • Experience with reinforcement learning algorithms for robotics: PPO, SAC, etc
  • Experience tuning hyperparameters and cost functions for these RL algorithms
  • Familiarity with common RL techniques such as: domain randomization, curriculum learning, reward shaping, etc.
  • Capable of leading complex controls projects and mentoring junior engineers

Bonus Qualifications:

  • Experience with behavior cloning techniques (e.g. distillation)

The US base salary range for this full-time position is between $150,000 and $350,000 annually.

The pay offered for this position may vary based on several individual factors, including job-related knowledge, skills, and experience. The total compensation package may also include additional components/benefits depending on the specific role. This information will be shared if an employment offer is extended. 

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