Software Engineer (Open Source Contributor)

Zekai

United States

Remote

USD 69,000 - 138,000

Full time

14 days+

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Benefits offered by this job

Remote contract
Worldwide applicants

Job summary

Zekai is seeking a Software Engineer (Open Source Contributor) for a remote contract role. You will build reproducible RL environments to test AI models for fixes, feature additions, refactors, and performance optimization using real-world coding tasks with verifiable solutions.

You will showcase open source contributions on GitHub (C++, Python, Java, Go, TypeScript, or Rust) and may undergo a HackerRank assessment if needed. This is a 100% remote, worldwide opportunity.

Qualifications

  • Proven open source contributions or a public profile showcasing code work.
  • Experience building backend services and APIs.
  • Strong debugging, performance tuning, and automated testing skills.

Responsibilities

  • Build reproducible Reinforcement Learning environments to test AI model fixes, new features, refactors, and performance improvements.
  • Create deterministic verifiers and golden references for problems.
  • Contribute expert-level code samples and debugging strategies in multiple languages.
  • Review peer-contributed code and ensure clarity and correctness of submissions.
  • Document technical decisions to improve AI training data quality.
  • Collaborate with cross-functional teams in a remote setup.

Skills

Backend experience
Go
Rust
Java
Python
C++
TypeScript
APIs
Debugging
GitHub

Tools

GitHub

Job description

Role Title: Software Engineer (Open Source Contributor)

Role Type: Contractor

Location: Anywhere in the world, remote

Opportunity Snapshot

Pay: $50–100/hour

Time per task: ~15 hours per run

Onboarding: As soon as you’re selected, Talent Ops will send you everything you need to get started

You’ll need: clear open source contributions and a profile to showcase them, such as GitHub (preferably with C++, Python, Java, Go, TypeScript, or Rust). If you don’t, you’ll be assessed through a HackerRank assessment instead.

Your tasks: Build reproducible Reinforcement Learning Environments that test an AI model’s ability to fix code, add features, refactor, and optimize performance - including a golden reference solution for each problem

Bonus: experts who complete the AI interview for this specific role move through the process significantly faster.

We care about your human judgment: our interview and assessment stages are designed to see how you think, so we ask that you complete them on your own, without AI assistants or help from others.

Job Description

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.

You'll create realistic, interesting and challenging coding tasks for AI to solve involving feature implementation and bug fixes, and implement deterministic verifiers that accept reasonable valid solutions and reject everything else.

What we're looking for
  • Strong backend experience in Java, Node.js, Go, Rust, Typescript, Python, C++ or similar
  • A track record building scalable services and APIs
  • Comfort with debugging, performance work and automated testing
  • Solid code review instincts
Preferred Qualifications
  • Proven experience in backend development, with a track record of delivering high-quality and performant projects.
  • Demonstrated success in identifying and resolving software bugs and system bottlenecks.
  • Experience with codebase refactoring and implementing complex features in large-scale applications.
  • Strong understanding of algorithms, data structures, and foundational computer science concepts.
  • Excellent analytical, problem-solving, and technical communication skills.
Scope of Work
  • 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.
Preferred Qualifications
  • Significant hands-on expertise in at least one of the following: Python3, Java, Rust, Go, C++, or TypeScript.
  • Deep understanding of algorithms, data structures, and software engineering principles.
  • Proven ability to debug complex systems, resolve bugs, and deliver effective optimizations.
  • Experience with large codebase refactoring and legacy system modernization.
  • Track record of delivering high-impact features from conception through delivery within cross-functional projects.
  • Strong documentation and communication skills to articulate technical concepts clearly.
  • Interest in or curiosity about AI and its technical challenges (previous AI experience not required).
Process:
  • Complete AI interview (aprox. 20 minutes)
  • Technical Assessment (if your GitHub profile doesn’t fully showcase the expertise required for this role)
  • 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.

This is a 100% remote contract role, open to applicants worldwide in the countries the employer accepts.

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