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AI Summary

You will create Reinforcement Learning Environments to test AI models on complex software engineering tasks like debugging and feature implementation. Additionally, you will document technical reasoning and validate peer-contributed code to improve AI training data quality.

Job Description

  • Job Title: GitHub Contributor
  • Job Type: Contractor
  • Location: Remote

About the hiring company:

Our client is a rapidly growing, venture-backed AI company helping shape the next generation of intelligent systems. By combining world-class human expertise with advanced machine learning workflows, they enable leading AI organizations to build, evaluate, and improve cutting-edge models used across a wide range of industries.

The company works with highly accomplished professionals in fields such as software engineering, finance, healthcare, legal, operations, research, and other specialized domains. These experts contribute directly to the development of advanced AI systems by providing real-world knowledge, evaluations, feedback, and domain-specific judgment that help models reason more accurately and perform more effectively.

Leveraging a proprietary AI-driven talent assessment and matching platform, the organization identifies exceptional professionals globally and connects them with high-impact projects at the forefront of artificial intelligence.

Backed by more than $40 million in funding and supported by a rapidly expanding international network of experts, the company is building critical human intelligence infrastructure for the AI economy and creating meaningful opportunities for professionals to apply their expertise in entirely new ways.

Job Summary:

Engaging expert Senior Software Engineers to support a customer’s innovative project. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters.

As an expert you will be creating Reinforcement Learning Environments which test and AI model's ability to solve complex software engineering problems related to fixing code, creating features, refactoring code and optimizing performance. You will be tasked with creating a reproducible environment and golden reference solution for the problem.

Applicants must have clear open source contributions and profiles to showcase it like GitHub or GitLab. (Preferably with C++, Python, JAVA, GoLang, Typescript, or Rust)

Scope of Work

  1. Contribute expert-level code samples, debugging strategies, and development insights in Python3, Java, Rust, Go, C++, or TypeScript.
  2. Analyze, troubleshoot, and resolve complex software defects and performance bottlenecks in diverse codebases.
  3. Implement robust new features, ensuring scalability, maintainability, and adherence to software best practices.
  4. Refactor and optimize legacy code to improve clarity, efficiency, and long-term reliability.
  5. Document technical reasoning, solution approaches, and code decisions to enhance AI training data quality.
  6. Review and validate peer-contributed code and technical submissions for accuracy and clarity.

Preferred Qualifications

  1. Significant hands-on expertise in at least one of the following: Python3, Java, Rust, Go, C++, or TypeScript.
  2. Deep understanding of algorithms, data structures, and software engineering principles.
  3. Proven ability to debug complex systems, resolve bugs, and deliver effective optimizations.
  4. Experience with large codebase refactoring and legacy system modernization.
  5. Track record of delivering high-impact features from conception through delivery within cross-functional projects.
  6. Strong documentation and communication skills to articulate technical concepts clearly.
  7. Interest in or curiosity about AI and its technical challenges (previous AI experience not required).

Process:

  1. Apply to the role, filling out the screening questions
  2. Complete AI interview (aprox. 30 minutes)
  3. Technical Assessment (Tentative)
  4. Hiring Manager review

 

Compensation Structure

Compensation is output-based; experts are paid per task that meets the project specifications. The time required to complete work may vary depending on the expert’s experience and workflow. Minimum submission requirements apply. Experts must submit a minimum of tasks per week.

Start Timeline & Availability

We typically fill roles within 48 hours and are looking for experts ready to jump in right away. If selected, we expect you to start your first tasks within 24–48 hours of completing onboarding.

Additional Information


A very attractive and competitive package is offered.

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