Remote GitHub Contributor: Code, Debug and Refactor

YO AI Labs

Illinois

Remote

USD 83,000 - 165,000

Full time

14 days+
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Job summary

YO AI Labs is seeking a GitHub Contributor for a remote contract role. You will contribute expert-level code samples, debugging strategies, and development insights across Python3, Java, Rust, Go, C++, or TypeScript.

You will analyze defects, implement robust features, refactor legacy code, and document reasoning to help improve AI training data quality and code quality within cross-functional projects.

Qualifications

  • Experience in at least one listed language.
  • Strong understanding of algorithms and data structures.
  • Proven debugging and optimization skills.
  • Experience refactoring large codebases.
  • Ability to document technical decisions clearly.
  • Interest in AI and its challenges.

Responsibilities

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

Skills

Python3
Java
Rust
Go
C++
TypeScript
Algorithms
Data structures
Debugging
Documentation
Communication
AI interest

Job description

YO AI Labs is seeking a GitHub Contributor for a remote contract role. You will contribute expert-level code samples, debugging strategies, and development insights across Python3, Java, Rust, Go, C++, or TypeScript.

You will analyze defects, implement robust features, refactor legacy code, and document reasoning to help improve AI training data quality and code quality within cross-functional projects.

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