Software Engineer (Open Source Contributor)

Weekday AI · Remote, United States

This role is for one of our clients

Pay: 50–100/hour
Role Type: Contractor
Location: Anywhere in the world, remote
Estimated Time Commitment:

Approximately 15 hours per task run
Start Timeline:

Within 24–48 hours of onboarding

We are seeking experienced

Software Engineers and Open Source Contributors

to help develop and evaluate next-generation AI systems through high-quality software engineering tasks. This opportunity is ideal for developers with strong programming fundamentals, practical experience contributing to open-source projects, and a proven ability to solve complex engineering problems.
In this role, you will design realistic, challenging, and reproducible coding environments that assess an AI models ability to fix bugs, implement new features, refactor existing code, and optimize software performance. You will also develop reliable verification mechanisms and golden reference solutions to evaluate whether AI-generated code meets the specified requirements.
Prior experience in artificial intelligence is not required. Your software engineering expertise, code quality standards, debugging skills, and open-source contributions will be central to the role.
Requirements
Key Responsibilities
Coding Task Development:

Create realistic, technically challenging programming tasks involving bug fixes, feature development, code refactoring, and performance optimization.
Reproducible Environment Setup:

Build deterministic reinforcement learning environments with the necessary dependencies, test suites, configurations, and execution instructions.
Reference Solution Implementation:

Develop reliable golden reference solutions that demonstrate the expected behavior and satisfy all task requirements.
Automated Verification:

Design deterministic evaluators and test cases that accept valid implementations while rejecting incorrect, incomplete, or non-compliant solutions.
Software Debugging:

Investigate complex software defects, troubleshoot unexpected behavior, and resolve performance bottlenecks across diverse codebases.
Feature Engineering:

Implement robust and scalable features while following established software development principles and coding standards.
Code Refactoring and Optimization:

Improve existing codebases by enhancing readability, maintainability, execution efficiency, and long-term reliability.
Code Review and Validation:

Review code contributions and technical submissions for correctness, clarity, performance, and adherence to best practices.
Technical Documentation:

Document problem statements, implementation decisions, debugging strategies, testing procedures, and solution reasoning to support high-quality AI training and evaluation.
Continuous Improvement:

Refine tasks and evaluation criteria based on observed model behavior and identified weaknesses.
Required Qualifications
Strong software engineering experience in one or more programming languages, including Python, Java, C++, Go, Rust, or TypeScript.
Demonstrated experience developing backend systems, scalable services, APIs, or complex software applications.
A clear track record of open-source contributions, preferably supported by a GitHub profile or equivalent public portfolio.
Strong understanding of data structures, algorithms, software design principles, and debugging techniques.
Practical experience with automated testing, code validation, and software quality assurance.
Ability to identify and resolve complex bugs, implementation errors, and performance bottlenecks.
Experience developing new features and improving existing codebases.
Strong analytical thinking, problem-solving, documentation, and technical communication skills.
Ability to work independently, follow detailed technical specifications, and deliver reproducible, verifiable solutions.
Interest in AI systems and understanding how software engineering expertise can support AI model training and evaluation.
Must-Have Skills
Software Engineering
Open Source Contributions
Backend Development and API Design
Python, Java, C++, Go, Rust, or TypeScript
Debugging and Troubleshooting
Data Structures and Algorithms
Automated Testing and Code Validation
Code Review and Refactoring
Performance Optimization
Good-to-Have Skills
Experience with large-scale or legacy codebases
Reinforcement Learning Environment Development
Deterministic Testing and Automated Evaluation
CI/CD and Development Tooling
Scalable Distributed Systems
Performance Profiling and Optimization
Complex Feature Implementation
AI Model Evaluation and Coding Agent Assessment
Public GitHub repositories or notable open-source projects
Opportunity Snapshot
Time Commitment:

Approximately 15 hours per task run, depending on complexity and individual workflow.
Flexible Schedule:

Work with flexibility while meeting task requirements and weekly submission minimums.
Getting Started:

Selected candidates receive onboarding instructions and the resources required to begin.
Portfolio Requirement:

A public GitHub profile or another portfolio demonstrating relevant open-source contributions is preferred.
Alternative Assessment:

Candidates whose public portfolios do not sufficiently demonstrate their skills may be required to complete a HackerRank technical assessment.
Compensation:

Payment is based on completed tasks that meet the specified quality and acceptance criteria.
Selection Process
Application:

Submit your application and complete the initial screening questions.
AI Interview:

Complete an approximately 20-minute interview focused on your technical experience and problem-solving approach.
Technical Assessment:

Complete a HackerRank assessment if your portfolio does not adequately demonstrate the required expertise.
Hiring Manager Review:

Your application, technical capabilities, and assessment results will be reviewed.
Onboarding:

Selected candidates receive onboarding materials and instructions for beginning their assignments.
Candidates are expected to complete interviews and assessments independently so their technical judgment and problem-solving abilities can be evaluated accurately.
Expected Availability
The selection process may move quickly, with positions often filled within approximately 48 hours. Successful candidates should be prepared to begin their first tasks within 24–48 hours after completing onboarding.