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Reinforcement Learning With Human Feedback Jobs in Arizona

YO AI Labs

Machine Learning Engineer - Remote

Phoenix, AZ · Remote

$80 - $120/hr

Create reinforcement learning environments for software engineering tasks. * Design tasks involving ... Experience with bug fixing and debugging complex software issues . * Proven experience in feature ...

TSMC

Senior AI Model Fine-Tuning Engineer

Phoenix, AZ · On-site

$128K - $176K/yr

Apply Reinforcement Learning from Human Feedback (RLHF) and other behavioral fine-tuning methods to improve the model's alignment with user needs and ethical standards. * Collaborate with data teams ...

YO AI Labs

AI/ML Software Engineer - Remote

Phoenix, AZ · Remote

$80 - $120/hr

Create reinforcement learning environments for software engineering tasks. * Design tasks involving ... Experience with bug fixing and debugging complex software issues . * Proven experience in feature ...

YO AI Labs

AI Technical Trainer - Remote

Phoenix, AZ · Remote

$80 - $120/hr

Create reinforcement learning environments for software engineering tasks. * Design tasks involving ... Experience with bug fixing and debugging complex software issues . * Proven experience in feature ...

YO AI Labs

Backend Engineer - Remote

Phoenix, AZ · Remote

$80 - $120/hr

Create reinforcement learning environments for software engineering tasks. * Design tasks involving ... Experience with bug fixing and debugging complex software issues . * Proven experience in feature ...

YO AI Labs

Backend Developer - Remote

Phoenix, AZ · Remote

$80 - $120/hr

Create reinforcement learning environments for software engineering tasks. * Design tasks involving ... Experience with bug fixing and debugging complex software issues . * Proven experience in feature ...

YO AI Labs

AI coding agents Expert - Remote

Phoenix, AZ · Remote

$80 - $120/hr

Create reinforcement learning environments for software engineering tasks. * Design tasks involving ... Experience with bug fixing and debugging complex software issues . * Proven experience in feature ...

YO AI Labs

AI Software Engineer - Remote

Phoenix, AZ · Remote

$80 - $120/hr

Create reinforcement learning environments for software engineering tasks. * Design tasks involving ... Experience with bug fixing and debugging complex software issues . * Proven experience in feature ...

YO AI Labs

AI Trainer - Remote

Phoenix, AZ · Remote

$80 - $120/hr

Create reinforcement learning environments for software engineering tasks. * Design tasks involving ... Experience with bug fixing and debugging complex software issues . * Proven experience in feature ...

TSMC

Senior AI Model Fine-Tuning Engineer

Phoenix, AZ · On-site

$128K - $176K/yr

Apply Reinforcement Learning from Human Feedback (RLHF) and other behavioral fine-tuning methods to improve the model's alignment with user needs and ethical standards. * Collaborate with data teams ...

YO AI Labs

SWE AI Training Data Expert - Remote

Phoenix, AZ · Remote

$80 - $120/hr

Create reinforcement learning environments for software engineering tasks. * Design tasks involving ... Experience with bug fixing and debugging complex software issues . * Proven experience in feature ...

YO AI Labs

Frontend Developer - Remote

Phoenix, AZ · Remote

$80 - $120/hr

Create reinforcement learning environments for software engineering tasks. * Design tasks involving ... Experience with bug fixing and debugging complex software issues . * Proven experience in feature ...

YO AI Labs

AI Engineer - Remote

Phoenix, AZ · Remote

$80 - $120/hr

Create reinforcement learning environments for software engineering tasks. * Design tasks involving ... Experience with bug fixing and debugging complex software issues . * Proven experience in feature ...

YO AI Labs

RL Environment Engineer - Remote

Phoenix, AZ · Remote

$80 - $120/hr

Create reinforcement learning environments for software engineering tasks. * Design tasks involving ... Experience with bug fixing and debugging complex software issues . * Proven experience in feature ...

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Reinforcement Learning With Human Feedback information

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.

What are popular job titles related to Reinforcement Learning With Human Feedback jobs in Arizona?

For Reinforcement Learning With Human Feedback jobs in Arizona, the most frequently searched job titles are:

What job categories do people searching Reinforcement Learning With Human Feedback jobs in Arizona look for?

The top searched job categories for Reinforcement Learning With Human Feedback jobs in Arizona are:

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Reinforcement Learning With Human Feedback jobs near you
Infographic showing various Reinforcement Learning With Human Feedback job openings in Arizona as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.

Machine Learning Engineer - Remote

YO AI Labs

Phoenix, AZ • Remote

$80 - $120/hr

Full-time

Posted 10 days ago

Job description

Senior Software Engineer

Job Type: Contractor (~15 hours/week)
Location: Remote

Job Summary

We are seeking experienced Senior Software Engineers to support an AI training project by creating reinforcement learning environments that evaluate AI models on complex software engineering tasks using Model Context Protocol (MCP) tools.

You will design reproducible environments, deterministic verification, and reference solutions for tasks such as bug fixing, feature implementation, codebase refactoring, and performance optimization. No prior AI experience is required.

Key Responsibilities
  • Create reinforcement learning environments for software engineering tasks.
  • Design tasks involving bug fixing, feature development, refactoring, and performance optimization.
  • Build deterministic verification systems and golden reference solutions.
  • Evaluate AI agents' ability to reason through complex codebases and use MCP tools effectively.
  • Develop realistic, reproducible software engineering scenarios.
  • Ensure tasks accurately measure coding ability, problem-solving, and tool usage.
  • Document solutions and provide clear technical feedback.
Required Skills
  • Strong proficiency in Python 3, Java, Rust, C++, or TypeScript.
  • Strong understanding of algorithms and data structures.
  • Experience with bug fixing and debugging complex software issues.
  • Proven experience in feature implementation and codebase refactoring.
  • Strong knowledge of performance optimization and tuning.
  • Excellent written and verbal communication.
  • Strong attention to detail.
Preferred Qualifications
  • Experience working with large-scale or distributed codebases.
  • Familiarity with AI/ML systems is a plus but not required.
  • Experience with rigorous code reviews and software engineering best practices.
  • Experience working effectively in remote or cross-functional teams.
Hiring Process
  1. Submit an application and screening questions.
  2. Complete an AI interview (~30 minutes).
  3. Complete a technical assessment, if required.
  4. Hiring Manager review.
Compensation

Compensation is output-based, with payment provided per task that meets project specifications. Minimum weekly submission requirements may apply.

Availability

Selected experts should be prepared to begin their first tasks within 24–48 hours of completing onboarding.