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Remote | Software Engineer (GitHub Contributor) — \$50–\$100/hour
About the position
This is a remote freelance/contract consulting opportunity for experienced software engineers and GitHub contributors to support advanced AI training and evaluation initiatives. The work combines backend development, open-source software expertise, algorithms, codebase refactoring, debugging, performance optimisation, automated testing, and technical evaluation. Prior experience with AI training projects is not required. Professionals will create realistic programming challenges, construct reproducible reinforcement learning environments, build deterministic verification systems, and deliver accurate reference solutions. Assignments may involve implementing and evaluating backend services, APIs, and application components; analysing unfamiliar codebases; debugging and refactoring software; validating solutions with automated tests; and documenting technical decisions and verification methods. The engagement is fully remote and open worldwide. Compensation is advertised at \$50–\$100 per hour, with output-based payment per completed task that meets project specifications. Tasks are estimated at approximately 15 hours per task/run, and minimum weekly task submission requirements apply. There are 100 openings. Selected professionals should be prepared to begin initial assignments within 24–48 hours after onboarding. Selection may include screening questions, an approximately 20-minute AI interview, a technical assessment where applicable, and a hiring manager review. Interviews and assessments must be completed independently, without AI assistants or help from other people. Work must not use confidential or proprietary information belonging to an employer, client, institution, or other third party.
Responsibilities
- Design realistic, technically challenging programming tasks based on practical software engineering scenarios.
- Develop self-contained coding problems involving bug fixes, feature implementation, refactoring, and performance optimisation.
- Analyse unfamiliar codebases and translate technical requirements into clear engineering objectives.
- Create challenges that reflect authentic software development workflows, system constraints, and engineering best practices.
- Build reproducible reinforcement learning environments for evaluating AI coding capabilities.
- Design deterministic verification systems that recognise valid software implementations.
- Develop comprehensive automated tests that reject incorrect solutions while accepting reasonable alternatives.
- Produce accurate golden reference solutions for programming challenges.
- Validate evaluation environments for reproducibility, technical correctness, and consistency with requirements.
- Develop and evaluate backend services, APIs, and supporting application components.
- Implement robust software features while maintaining scalability, reliability, and code quality.
- Apply algorithms, data structures, and software design principles to engineering problems.
- Assess code architecture, system behaviour, and implementation decisions against technical requirements.
- Investigate software defects, implementation errors, and performance bottlenecks across codebases.
- Use systematic debugging to identify underlying issues and implement corrections.
- Refactor existing and legacy code to improve readability, maintainability, efficiency, and reliability.
- Evaluate computational performance, runtime behaviour, and resource utilisation.
- Verify engineering improvements through automated testing, reproducible execution, and structured analysis.
- Review programming solutions and technical submissions for correctness, clarity, efficiency, and maintainability.
- Evaluate peer-contributed code and provide precise, actionable engineering feedback.
- Document technical reasoning, implementation decisions, debugging strategies, and verification methodologies.
- Communicate software engineering concepts through clear written explanations and structured evaluations.
- Maintain technical accuracy, reproducibility, and documentation quality across assigned deliverables.
Requirements
- Significant hands-on professional software engineering experience, particularly in backend development and complex application systems.
- Demonstrated expertise in at least one of Python 3, Java, Rust, Go, C++, or TypeScript.
- Strong understanding of algorithms, data structures, computational complexity, and foundational computer science principles.
- Proven experience developing scalable backend services, APIs, and production software applications.
- Practical expertise in debugging, automated testing, code review, and technical problem-solving.
- Ability to identify and resolve complex software defects and system performance bottlenecks.
- Experience implementing substantial features within existing applications or large-scale codebases.
- Strong understanding of software architecture, refactoring, maintainability, and legacy system modernisation.
- Experience designing reproducible tests and validating implementations against technical specifications.
- Ability to develop objective acceptance criteria and explain why a solution meets defined requirements.
- Strong analytical reasoning, logical thinking, algorithmic problem-solving, and independent engineering judgement.
- Ability to navigate unfamiliar codebases and understand complex technical requirements efficiently.
- Excellent technical documentation skills, including clear explanations of programming decisions and software modifications.
- Strong written communication and constructive code review capabilities.
- Experience working independently on technical assignments and producing reproducible software deliverables.
- Must complete interviews and technical assessments independently, without AI assistants or help from other individuals.
- Must not use confidential or proprietary information belonging to an employer, client, institution, or other third party.
Nice-to-haves
- Demonstrable GitHub or comparable open-source contributions; candidates without a sufficiently representative portfolio may complete an alternative HackerRank assessment.
- Experience contributing to open-source repositories, reviewing pull requests, or maintaining collaborative software projects.
- Familiarity with reinforcement learning environments, coding benchmarks, or AI software engineering evaluation.
- Interest in advanced AI technologies and automated programming systems.
