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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 ...

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 ...

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 ...

Apptronik

Senior Reinforcement Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

... 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 ...

webAI Inc

Staff R&D AI Engineer

Austin, TX · On-site

Experience with RLHF implementation and human feedback integration for model alignment * Background in imitation learning, inverse reinforcement learning, or learning from demonstrations * Experience ...

Dexmate

Reinforcement learning engineer

Santa Clara, CA · On-site

Dexmate is building the foundation for physical AI -- a unified platform that combines high-quality robotic hardware with a universal Physical AI OS. They are seeking Reinforcement Learning experts ...

MLabs

Reinforcement Learning Engineer

New York, NY

$87K - $118K/yr

Recruiter / HR Call: Initial screening to discuss professional background, risk management ... A strategic discussion with leadership focusing on mission alignment, role expectations, and ...

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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 Jul 15, 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
Infographic showing various Reinforcement Learning With Human Feedback job openings in the United States as of July 2026, with employment types broken down into 82% Full Time, 17% Part Time, and 1% Contract. Highlights an 90% Physical, 1% Hybrid, and 9% Remote job distribution, with an average salary of $84,648 per year, or $40.7 per hour.

Machine Learning Engineer, Reinforcement Learning

Skild AI

Pittsburgh, PA • On-site

$100K - $300K/yr

Full-time

Re-posted 25 days ago

Job description

Company Overview
At Skild AI, we are building the world's first general purpose robotic intelligence that is robust and adapts to unseen scenarios without failing. We believe massive scale through data-driven machine learning is the key to unlocking these capabilities for the widespread deployment of robots within society. Our team consists of individuals with varying levels of experience and backgrounds, from new graduates to domain experts. Relevant industry experience is important, but ultimately less so than your demonstrated abilities and attitude. We are looking for passionate individuals who are eager to explore uncharted waters and contribute to our innovative projects.
Position Overview
We are looking for a Machine Learning Engineer to be responsible for designing and implementing cutting-edge reinforcement learning algorithms, conducting experiments, and optimizing these models to perform efficiently in real-world robotic environments. This will require close collaboration with our robotics, research, and engineering team. Your work will directly impact the development of intelligent, adaptable robots capable of learning and performing complex tasks autonomously.
Responsibilities
  • Develop and implement state-of-the-art reinforcement learning algorithms for robotic applications.
  • Design and conduct experiments to train RL models and conduct real-world tests.
  • Collaborate closely with researchers to explore novel methods of scaling up reinforcement learning model training.
  • Communicate effectively with inference, application, and deployment engineers to integrate RL models into robotic systems and iterate on methods to enable robust deployment.
  • Analyze and interpret experimental results, iterating on model design to achieve desired performance.
  • Stay up-to-date with the latest research and advancements in reinforcement learning.
Preferred Qualifications
  • BS, MS or higher degree in Computer Science, Robotics, Engineering or a related field, or equivalent practical experience.
  • Proficiency in Python, C++, or similar and at least one deep learning library such as PyTorch, TensorFlow, JAX, etc.
  • Deep understanding and practical experience with various reinforcement learning algorithms and techniques (model-free, model-based, multi-task, hierarchical, multi-agent, etc.).
  • Strong background in algorithms, data structures, and software engineering principles.
  • Experience with physics simulation engines and tools for training RL.
  • Deep understanding of state-of-the-art machine learning techniques and models.
  • Extensive industry experience with reinforcement learning and robotic systems.

Base Salary Range
$100,000-$300,000 USD

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