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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 ...

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

As of Aug 28, 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 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.

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.

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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 1% As Needed, 75% Full Time, 23% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $84,648 per year, or $40.7 per hour.

Threat Intel - AI / LLM Trainer - Make Your Own Hours

Remotasks

Remote

$49K - $66K/yr

Full-time

Re-posted 11 days ago

Job description

Job Title: AI Response Evaluator for Cyber Security Threat Intelligence

Location: Remote

Job Type: Task-Based Contract (Make your own hours: 5 hours per week minimum)

Overview:

We are seeking a highly skilled and motivated individual to join our team as an AI Response Evaluator with a focus on Cyber Security Threat Intelligence. This position is at the forefront of integrating Artificial Intelligence with Cyber Security to enhance threat intelligence and incident response capabilities. The successful candidate will be involved in evaluating AI-generated prompts using reinforcement learning with human feedback to grade and improve AI quality. This role is an exceptional opportunity to work at the cutting edge nexus of AI and Cyber Security, contributing to the development of smarter, more efficient systems capable of defending against the ever-evolving cyber threat landscape.

Responsibilities:

- Evaluate AI-generated prompt responses for accuracy, relevance, and effectiveness in the context of Cyber Security Threat Intelligence.

- Use reinforcement learning with human feedback to grade AI prompt responses and guide the AI's learning process for improved performance.

- Collaborate with cybersecurity professionals and data scientists to refine AI models based on real-world cyber threat intelligence and incident data.

- Participate in the continuous improvement of AI training processes, including the selection of training data and feedback mechanisms.

- Stay abreast of the latest developments in cyber security threats, tactics, techniques, and procedures (TTPs) to ensure the AI's outputs are relevant and up-to-date.

- Provide insights and feedback on the AI's performance and potential areas for enhancement in detecting and responding to cyber threats.

Preferred Certifications:

  - SANS GCIH (GIAC Certified Incident Handler)

  - SANS GCTI (GIAC Cyber Threat Intelligence)

  - Other relevant certifications in information security incident handling, vulnerability management, and threat intelligence are highly desirable (e.g., CISSP, CEH, OSCP).

Required Experience:

Candidates should have significant (multiple years) of experience in at least one of the following areas:

- Analyzing, triaging, and responding to cyber security incidents and investigations, particularly in response to recent vulnerabilities. This includes knowledge of threat actor group tactics, techniques, and procedures (TTPs) and how to detect and defend against them.

- Assessing Cyber Threat Intelligence and determining applicable risk for an organization via threat modeling. This involves understanding threat actor group TTPs and how to detect and defend against them.

- Identifying, managing, and remediating vulnerabilities, especially in response to well-known vulnerabilities exploited by threat actor groups.

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