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Reinforcement Learning With Human Feedback Jobs (NOW HIRING)

Apptronik

Senior Reinforcement Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

... human demonstration data (mocap, teleoperation) into robust reference trajectories for reinforcement learning. Qualifications : Required : • Deep, hands-on expertise (5+ years) with common RL ...

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

As of Jun 9, 2026, the average hourly pay for reinforcement learning with human feedback in the United States is $40.70, according to ZipRecruiter salary data. Most workers in this role earn between $29.57 and $52.88 per hour, depending on experience, location, and employer.

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

To excel as a Reinforcement Learning with Human Feedback (RLHF) Engineer, you need a strong background in machine learning, reinforcement learning theory, statistics, and typically an advanced degree in computer science or a related field. Familiarity with deep learning frameworks (such as TensorFlow or PyTorch), RL libraries (like Ray RLlib), and experience with data collection and annotation systems are essential. Excellent problem-solving abilities, communication skills, and teamwork help you collaborate with researchers, data annotators, and other engineers. These skills enable you to design and implement RLHF systems that are robust, scalable, and aligned with human values.

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

AspectReinforcement Learning With Human FeedbackReinforcement Learning Engineer
CredentialsTypically requires knowledge of machine learning, AI, and data analysisRequires similar credentials in machine learning, programming, and AI
Work EnvironmentResearch labs, AI development teams, tech companiesDevelopment teams, research labs, tech firms
Industry UsageUsed in AI training, human-in-the-loop systems, and model refinementDesigning, implementing, and optimizing reinforcement learning algorithms

Reinforcement Learning With Human Feedback focuses on improving AI models through human input, while Reinforcement Learning Engineers develop and deploy these algorithms. Both roles require strong machine learning skills and often work in similar environments, but their core responsibilities differ in application and focus.

What is Reinforcement Learning with Human Feedback?

Reinforcement Learning with Human Feedback (RLHF) is a machine learning technique where AI agents are trained not only through automated reward signals but also by incorporating feedback from humans. This approach helps align the agent’s behavior with human preferences, values, or safety requirements by allowing humans to guide or correct the learning process. RLHF is commonly used in developing advanced AI systems, such as language models, to ensure their outputs are helpful, safe, and aligned with user expectations. The process often involves human evaluators ranking or scoring the AI's responses, which are then used to fine-tune the model’s behavior.

What are the typical collaborations involved for a Reinforcement Learning with Human Feedback (RLHF) specialist within a machine learning team?

As an RLHF specialist, you often work closely with data scientists, machine learning engineers, and domain experts to design effective feedback mechanisms and reward models. Collaboration with annotation teams or subject matter experts is common, as high-quality human feedback is crucial for training robust RLHF models. You may also partner with product managers and UX researchers to ensure that the models align with user needs and ethical considerations. Regular cross-functional meetings and code reviews help maintain alignment and foster innovation across teams.
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Reinforcement Learning With Human Feedback jobs near you
Staff Reinforcement Learning Engineer - Whole Body Control

Staff Reinforcement Learning Engineer - Whole Body Control

Figure

San Jose, CA • On-site

$200K - $300K/yr

Full-time

Medical

Posted yesterday

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 Staff 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 $200,000 and $300,000 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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