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Reinforcement Learning With Human Feedback Jobs (NOW HIRING)

MLabs

Reinforcement Learning Engineer

New York, NY · On-site

$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 ...

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 ... Our flagship humanoid robot, Apollo, is built to collaborate thoughtfully with people, starting ...

MLabs

Reinforcement Learning Engineer

New York, NY · On-site

$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 ...

Figure

Helix AI Engineer, Robot Learning

San Jose, CA · On-site

$200K - $400K/yr

The goal of the company is to ship humanoid robots with human level intelligence. Its robots are ... Apply and extend techniques including behavior cloning, reinforcement learning, and VLA reasoning

Figure

Helix AI Engineer, Robot Learning

San Jose, CA · On-site

$200K - $400K/yr

The goal of the company is to ship humanoid robots with human level intelligence. Its robots are ... Apply and extend techniques including behavior cloning, reinforcement learning, and VLA reasoning

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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.
Senior Machine Learning Engineer, Reinforcement Learning - Egofold

Senior Machine Learning Engineer, Reinforcement Learning - Egofold

Snail Games USA

Beverly Hills, CA • On-site, Remote

$150K - $185K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 23 days ago

Job description

Senior Machine Learning Engineer, Reinforcement Learning – Egofold

About Snail Games USASnail Games strives to create the new high bar for gameplay experience in online gaming. We have been a global developer and publisher of digital entertainment since 2009 and are committed to pushing the boundaries of the industry.

About EgofoldEgofold is an AI initiative within Snail Games focused on intelligent agents, simulation, and AI-driven workflows for interactive products. It operates with startup-style speed and broad ownership, backed by an established game company, and is currently building practical prototypes while shaping its longer-term direction.

About the RoleWe are looking for a Senior Machine Learning Engineer with strong depth in machine learning and practical experience applying reinforcement learning and related methods to agent behavior and decision systems. This role is focused on the ML core of Egofold: designing experiments, training and improving models, shaping evaluation loops, and helping successful approaches become usable parts of the broader project.

This is not a siloed research role. The best candidates stay engaged through evaluation, iteration, and practical integration, and bring enough adjacent breadth to be effective in a small, collaborative team. We value curiosity, ownership, sound judgment, and respectful, low-ego collaboration.

Job Type: Full-TimeLocation: Hybrid – Los Angeles Area (1–2 in-office meetings per month)

Responsibilities

  • Design, train, and iterate on machine learning models for intelligent agents and decision-making systems, with an emphasis on reinforcement learning and related approaches.

  • Define and refine state representations, action spaces, reward structures, and evaluation criteria to improve agent behavior.

  • Build and improve practical experimentation and training workflows, including data generation, experiment tracking, and reproducibility.

  • Analyze results, debug model behavior, and make pragmatic tradeoffs between model performance, iteration speed, and system complexity.

  • Work closely with engineers and other partners to help integrate successful ML work into usable product systems.

  • Contribute thoughtful technical input on next-step experiments, tooling, and ML direction as Egofold continues to evolve.

Minimum Requirements

  • Strong foundation in machine learning, with hands-on experience building, training, and iterating on applied ML systems.

  • Professional or substantial project experience with reinforcement learning, agent-based systems, sequential decision-making, or closely related areas.

  • Strong Python skills and experience with modern ML frameworks such as PyTorch.

  • Experience designing experiments, evaluating model behavior, and improving results through systematic iteration.

  • T-shaped capability: deep machine learning expertise plus practical range across one or more adjacent areas such as simulation, evaluation, model integration, systems collaboration, or robotics-adjacent machine learning.

  • Strong problem-solving ability, sound judgment, and comfort working in ambiguous, fast-changing environments.

  • Respectful, low-ego collaborative style and willingness to work beyond a narrow specialty when the work requires it.

Nice to Have Any of the following are valuable, but we do not expect depth in every area:

  • Experience with reinforcement learning methods such as PPO, SAC, DQN, actor-critic, or related approaches.

  • Familiarity with simulation environments, multi-agent systems, game AI, or interactive agent behaviors.

  • Familiarity with C++, inference runtimes, or collaborating with engineers who deploy machine learning models into production systems.

  • Exposure to robotics, embodied AI, or embedded / on-device machine learning constraints.

Salary Range: $150,000 – $185,000 Annually

Why Join the Snail Games USA Team?

  • True focus on work/life balance

  • Paid company holidays, vacation, and separate sick leave

  • Medical, dental, vision, and Life/LTD

  • 401k with company match

Work Authorization Requirements

Applicants must be legally authorized to work in the United States at the time of application. This position does not offer visa sponsorship now or in the future (including H-1B).

Additional Information

As part of the Company’s activities in video game development, publishing, and short-form video content creation, certain projects, discussions, or creative materials may include themes, visuals, language, or subject matter that some individuals could find mature, violent, sexual, graphic, or otherwise sensitive in nature (collectively referred to as “Mature Content”). Examples may include, but are not limited to, depictions or descriptions of combat, violence, adult themes or relationships, suggestive or satirical humor, or strong language. Employees are expected to engage with such material in a professional and creative context as part of their job duties.