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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 ...

webAI Inc

Staff R&D AI Engineer

Austin, TX · On-site +1

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

Apptronik

Senior Reinforcement Learning Engineer

Austin, TX

$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 ...

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 ...

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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 AI Engineer

Reinforcement Learning AI Engineer

Booz Allen Hamilton

Chantilly, VA • On-site

$99K - $225K/yr

Full-time

Medical, Life, Retirement, PTO

Posted just now

Booz Allen Hamilton rating

8.8

Company rating: 8.8 out of 10

Based on 47 frontline employees who took The Breakroom Quiz

9th of 57 rated business consultants

Job description

Reinforcement Learning AI Engineer

The Opportunity:

Booz Allen is seeking an innovative and experienced AI developer specializing in reinforcement learning to join our growing team for Space solutions. In this role, you will leverage your expertise in artificial intelligence, data science, and machine learning engineering to train, test, deploy, and maintain models that learn from data.You will collaborate with cross-functional teams to translate reinforcement learning research into operational capability and production-grade code, bringing significant technological advancements that drive mission success.

You'll pioneer a growing community of machine learning engineers across the company.You'll collaborate with a team of dedicated Space, Military, Intelligence, Engineering, and AI professionals to deliver bleeding-edge solutions to solve high-priority national defense problems.

What You'll Work On:

  • Design, implement, and train reinforcement learning (RL) and multi-agent reinforcement learning (MARL) algorithms for complex decision-making problems.

  • Develop scalable training pipelines using Python and modern ML frameworks.

  • Build and evaluate agents in simulated environments using Gym or PettingZoo, high-fidelity simulators, or custom environments.

  • Apply RL techniques such as policy optimization, value-based learning, model-based RL, and imitation learning.

  • Collaborate with domain experts to define reward structures, constraints, and evaluation metrics aligned with mission objectives.

  • Implement distributed training workflows leveraging cloud compute, containerization, and orchestration technologies.

  • Transition trained models into production systems, following strong software engineering best practices.

  • Contribute to system architecture and performance optimization in Python with opportunities to extend into C++ or Rust for high-performance components.

Join us. The world can't wait.

You Have:

  • Experience developing and training reinforcement learning agents

  • Experience with Gym or PettingZoo interfaces

  • Experience with ML frameworks such as PyTorch, TensorFlow, or JAX

  • Experience with artificial intelligence, data science, machine learning engineering, or software engineering

  • Experience developing technical solutions using Python, C++, or Rust

  • Knowledge of reinforcement learning and artificial neural networks

  • Secret clearance

  • Bachelor's degree in a Computer Science, Artificial Intelligence, or Engineering field

Nice If You Have:

  • Experience applying RL to autonomy, control systems, or mission-scale

  • Experience with Multi-Agent Reinforcement Learning (MARL)

  • Experience with AFSIM or other high-fidelity simulation environments

  • Experience with embedded systems programming in C, C++, or Rust

  • Experience in GPU programming, including CUDA or RAPID

  • Experience developing in-space solutions

  • Knowledge of modern software design patterns, including microservice design and orchestration in Kubernetes deployment

  • Master's degree in Computer Science, Artificial Intelligence, Engineering, or a related field

Clearance:

Applicants selected will be subject to a security investigation and may need to meet eligibility requirements for access to classified information; Secret clearance is required.

Compensation

At Booz Allen, we celebrate your contributions, provide you with opportunities and choices, and support your total well-being. Our offerings include health, life, disability, financial, and retirement benefits, as well as paid leave, professional development, tuition assistance, work-life programs, and dependent care. Our recognition awards program acknowledges employees for exceptional performance and superior demonstration of our values. Full-time and part-time employees working at least 20 hours a week on a regular basis are eligible to participate in Booz Allen's benefit programs. Individuals that do not meet the threshold are only eligible for select offerings, not inclusive of health benefits. We encourage you to learn more about our total benefits by visiting the Resource page on our Careers site and reviewing Our Employee Benefits page.

Salary at Booz Allen is determined by various factors, including but not limited to location, the individual's particular combination of education, knowledge, skills, competencies, and experience, as well as contract-specific affordability and organizational requirements. The projected compensation range for this position is $99,000.00 to $225,000.00 (annualized USD). The estimate displayed represents the typical salary range for this position and is just one component of Booz Allen's total compensation package for employees. This posting will close within 90 days from the Posting Date.

Identity Statement

As part of the hiring process, we will ask you to complete an identity verification process that leverages advanced biometrics and artificial intelligence to ensure authenticity and protect against identity fraud. You are expected to be on camera during interviews and assessments. We reserve the right to take your picture to verify your identity and prevent fraud.

Candidate AI Usage Policy

AI is a part of our daily work at Booz Allen, and we are committed to the responsible and ethical use of AI tools. However, we want to ensure a fair candidate process based on your own skills and knowledge. As part of this commitment, the use of artificial intelligence (AI) or other tools to assist with responses during interviews (whether in-person or virtual) is prohibited unless permission is explicitly provided.

Work Model
Our people-first culture prioritizes the benefits of collaboration, whether it occurs in person or virtually. To support engagement and effective communication, employees working virtually are generally expected to have their cameras on during meetings.

  • Remote: If this position is listed as remote, there may still be occasions when you are required to work in person at a Booz Allen or customer facility.

  • Hybrid: If this position is listed as hybrid, you will be expected to work from a Booz Allen facility frequently, in alignment with leadership expectations and the needs of the role. You may also be required to work from or visit a customer facility.

  • Onsite: If this position is listed as onsite, work will primarily be performed at a Booz Allen office or customer facility, where employees will collaborate directly with colleagues and customers as required by the role.

Commitment to Non-Discrimination

All qualified applicants will receive consideration for employment without regard to disability, status as a protected veteran or any other status protected by applicable federal, state, local, or international law.

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About Booz Allen Hamilton

Sourced by ZipRecruiter

Booz Allen Hamilton is a leading provider of management and technology consulting services to the US government in defense, intelligence, and civil markets. Headquartered in McLean, Virginia, the firm also serves major corporations, institutions, and not-for-profit organizations. Founded in 1914 by Edwin G. Booz, the company has a long-standing tradition of helping clients achieve success by delivering a wide range of consulting services that include strategic planning, human capital and learning, communication, systems development, and others. The company's mission is to empower people to change the world, and it has a reputation for maintaining the highest standards of integrity and-excellence.

Industry

It services

Company size

10,000+ Employees

Headquarters location

McLean, VA, US

Year founded

1914

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