ZipRecruiter

Log In

1

Reinforcement Learning Human Feedback Jobs (NOW HIRING)

Apptronik

Senior Reinforcement Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

Develop and refine motion retargeting pipelines to translate human demonstration data (mocap, teleoperation) into robust reference trajectories for reinforcement learning. SKILLS AND REQUIREMENTS

Apptronik

Senior Reinforcement Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

Apptronik is a human-centered robotics company developing AI-powered robots to support humanity in ... JOB SUMMARY The Senior Reinforcement Learning Engineer is a key, hands-on role focused on achieving ...

previous pagenext page

Showing results 21-40

All JobsReinforcement Learning Human Feedback Jobs

Reinforcement Learning Human Feedback information

See salary details

$11

$22

$38

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.

Senior Reinforcement Learning Engineer

Austin, TX • On-site

Apptronik
Industrial Automation Equipment Manufacturing • 11 - 50 employees
Learn more about Apptronikexternal

$103K - $142K/yr

Full-time

Re-posted 15 days ago

Key responsibilities

  • Implement and deploy state-of-the-art reinforcement learning algorithms to achieve high performance on locomotion and manipulation tasks with physical hardware.

  • Drive the development cycle from simulation prototyping to transferring and fine-tuning policies on robots.

  • Mentor junior engineers by providing technical guidance, conducting code reviews, and sharing best practices.

Job description

JOB SUMMARY

The Senior Reinforcement Learning  Engineer is a key, hands-on role focused on achieving state-of-the-art performance on our humanoid robots. This engineer will leverage their deep expertise in RL to solve critical locomotion and manipulation challenges and deliver breakthrough results on physical hardware. The primary focus of this role is to rapidly implement, iterate, and deploy advanced learning algorithms to push the boundaries of what our robots can do. As a senior member of the team, this individual will also be responsible for mentoring junior engineers, elevating the team's overall technical capabilities through their guidance and expertise.

ESSENTIAL DUTIES AND RESPONSIBILITIES or KEY ACCOUNTABILITIES

  • Implement and deploy state-of-the-art RL algorithms to achieve ambitious, world-class performance on dynamic locomotion and manipulation tasks with physical hardware.
  • Drive the entire development cycle, from prototyping in simulation to robustly transferring and fine-tuning policies on the robot.
  • Optimize and scale the RL training pipeline for faster iteration, contributing to core infrastructure for high-throughput simulation and distributed training.
  • Mentor junior engineers by providing technical guidance, conducting insightful code reviews, and sharing best practices in reinforcement learning and software development.
  • Collaborate closely with the robotics and hardware teams to diagnose system-level issues and co-develop solutions that enable more complex learned behaviors.
  • Analyze and present hardware results to guide future technical directions and demonstrate progress on key company objectives.
  • Develop and refine motion retargeting pipelines to translate human demonstration data (mocap, teleoperation) into robust reference trajectories for reinforcement learning.

SKILLS AND REQUIREMENTS

  • Deep, hands-on expertise (5+ years) with common RL frameworks (e.g., PyTorch, JAX) and high-fidelity physics simulators (e.g., MuJoCo, IsaacGym)
  • Mastery of Python for rapid prototyping and training, alongside strong proficiency in C++ for developing performant, deployable code.
  • Experience building or utilizing large-scale, distributed training pipelines and a strong intuition for their optimization.
  • A strong theoretical understanding of modern reinforcement learning, including deep expertise in areas like imitation learning, model-based RL, and sim-to-real transfer techniques.
  • A strong intuition for robot dynamics and controls theory, with the ability to apply these principles to guide and constrain learning-based approaches.
  • A results-oriented mindset with a passion for seeing complex algorithms work on real-world hardware.

EDUCATION and/or EXPERIENCE

  • A PhD or MS in Computer Science, Robotics, or a related field, with 2+ years industry experience strongly preferred.
  • A proven track record of successfully deploying learning-based policies on physical robotic systems, especially legged robots or manipulators.
  • Demonstrated experience mentoring or providing technical guidance to other engineers in a team environment.
  • A strong publication record in relevant conferences or journals (e.g., CoRL, RSS, ICRA) is a significant plus.

PHYSICAL REQUIREMENTS 

  • Prolonged periods of sitting at a desk and working on a computer
  • Must be able to lift 15 pounds at times
  • Vision to read printed materials and a computer screen
  • Hearing and speech to communicate

Apply