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Reinforcement Learning With Human Feedback Jobs (NOW HIRING)

FocusKPI Inc.

LLM Research Engineer

Mountain View, CA · On-site

Knowledge of reinforcement learning and RLHF (Reinforcement Learning with Human Feedback). **No C2C resumes are considered** Thank you! FocusKPI Hiring Team Founded in 2010, FocusKPI, Inc. (FocusKPI ...

FocusKPI Inc.

LLM Research Engineer

Mountain View, CA · On-site

$115 - $131/hr

Knowledge of reinforcement learning and RLHF (Reinforcement Learning with Human Feedback). **No C2C resumes are considered** Thank you! FocusKPI Hiring Team Founded in 2010, FocusKPI, Inc. (FocusKPI ...

Centific

Applied Reinforcement Learning Engineer

Reward model training, preference learning, human feedback integration • Direct optimization: DPO ... Software engineering beyond research with scalable pipelines and training infrastructure • ...

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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Reinforcement Learning With Human Feedback information

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

As of Aug 11, 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 engineer?

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 collaborations are typical for a reinforcement learning with human feedback 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
Infographic showing various Reinforcement Learning With Human Feedback job openings in the United States as of August 2026, with employment types broken down into 5% Internship, 65% Full Time, 20% Part Time, and 10% Contract. Highlights an 100% In-person job distribution, with an average salary of $84,648 per year, or $40.7 per hour.

Staff Reinforcement Learning Engineer - Whole Body Control

Figure

San Jose, CA • On-site

Full-time

Re-posted 28 days ago

Job description

Job Summary:
Figure is an AI Robotics company focused on developing autonomous general-purpose humanoid robots with human-level intelligence. They are seeking a Staff Reinforcement Learning Engineer to develop, train, deploy, and evaluate advanced reinforcement learning algorithms for the whole body control of their humanoid robots.
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
Qualifications:
Required:
• 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
Preferred:
• Experience with behavior cloning techniques (e.g. distillation)
Company:
Figure is an AI robotics company that develops autonomous general-purpose humanoid robots. Founded in 2022, the company is headquartered in San Jose, USA, with a team of 201-500 employees. The company is currently Growth Stage.

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