Software Engineer (Open Source Contributor)

Weekday AI

21h•$50 - $100•Remote

About The Position

This contractor role supports a client developing and evaluating next-generation AI systems through software engineering tasks. The work involves creating realistic, challenging, and reproducible coding environments that assess an AI model’s ability to fix bugs, implement features, refactor code, and optimize performance. The engineer also develops verification mechanisms and golden reference solutions to evaluate AI-generated code. Prior AI experience is not required; software engineering expertise, code quality, debugging skills, and open-source contributions are central to the role. The estimated time commitment is approximately 15 hours per task run, depending on complexity and workflow. The schedule is flexible while meeting task requirements and weekly submission minimums. Payment is based on completed tasks that meet quality and acceptance criteria. Selected candidates receive onboarding instructions and resources, and should be ready to begin their first tasks within 24–48 hours after onboarding.

Requirements

Strong software engineering experience in one or more of 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.
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.
Candidates are expected to complete interviews and assessments independently.

Nice To Haves

Experience with large-scale or legacy codebases.
Experience developing reinforcement learning environments.
Experience with deterministic testing and automated evaluation.
Experience with CI/CD and development tooling.
Experience with scalable distributed systems.
Experience with performance profiling and optimization.
Experience implementing complex features.
Experience with AI model evaluation and coding agent assessment.
A public GitHub profile or another portfolio demonstrating relevant open-source contributions.
Notable open-source projects.

Responsibilities

Create realistic, technically challenging programming tasks involving bug fixes, feature development, code refactoring, and performance optimization.
Build deterministic reinforcement learning environments with necessary dependencies, test suites, configurations, and execution instructions.
Develop reliable golden reference solutions that demonstrate expected behavior and satisfy task requirements.
Design deterministic evaluators and test cases that accept valid implementations and reject incorrect, incomplete, or non-compliant solutions.
Investigate complex software defects, troubleshoot unexpected behavior, and resolve performance bottlenecks across diverse codebases.
Implement robust and scalable features while following established software development principles and coding standards.
Improve existing codebases by enhancing readability, maintainability, execution efficiency, and long-term reliability.
Review code contributions and technical submissions for correctness, clarity, performance, and adherence to best practices.
Document problem statements, implementation decisions, debugging strategies, testing procedures, and solution reasoning.
Refine tasks and evaluation criteria based on observed model behavior and identified weaknesses.