Open Source Software Engineer for AI Model Training

Build reproducible Reinforcement Learning environments that evaluate an AI model''s ability to execute complex software engineering workflows. You will design environments that mirror real DevOps, CI/CD, and debugging tasks using common command line tools, and deliver golden reference solutions that define correct behavior.

• Create reproducible RL environments that test models on end-to-end software engineering workflows, including DevOps, CI/CD, debugging, and media processing scenarios.
• Implement golden reference solutions that demonstrate correct completion of each workflow.
• Contribute production-quality code, code reviews, and documentation to open-source repositories tied to the project.
• Design, implement, and optimize algorithms and system components using one or more of these languages: C++, Python, Java, Go, TypeScript, Rust.
• Identify and fix technical issues, bugs, and performance bottlenecks in existing codebases.
• Collaborate with other contributors and stakeholders to align deliverables with project goals and engineering best practices.
• Create and maintain technical documentation to support onboarding and knowledge sharing.
• Participate in code discussions, provide constructive feedback, and help raise overall code quality.
• Work with command line tools and libraries commonly used in these workflows, for example git, docker, gdb, asan, ffmpeg, and similar utilities.

• Demonstrable open source contributions, with public profiles such as GitHub or GitLab that clearly show your work.
• Proficiency in one or more of the following: C++, Python 3, Java, Go, TypeScript, Rust (basic knowledge acceptable for Rust).
• Experience designing reproducible test environments, or building tooling for CI/CD, debugging, or systems automation.
• Comfort contributing to and reviewing code in large codebases, and following rigorous code review practices.
• Previous work on large-scale, distributed systems is preferred but not required.
• Familiarity with AI or machine learning systems is a plus, but not mandatory.

• Role type: Contractor.
• Location: Remote.
• Output expectations: Compensation is output-based, paid per task that meets the project specifications.
• Minimum submissions: Experts must meet a minimum number of task submissions per week, this requirement will be specified with the engagement.
• Start timeline: Roles are typically filled within 48 hours. If selected, you should be ready to begin your first tasks within 24 to 48 hours after completing onboarding.

• Hourly range indicated: $50 to $150 per hour.
• Actual pay is task based, experts are paid per completed task that satisfies project requirements. The time required per task will vary by complexity and by expert workflow.
• Minimum submission requirements apply and influence earnings potential.

• Apply to the role and complete the screening questions.
• Complete an AI-driven interview, approximately 30 minutes in length.
• Complete a technical assessment, if requested.
• Hiring manager review will follow assessments.
• On successful selection and onboarding you will be asked to begin assigned tasks within 24 to 48 hours.

• Applicants must have public open source contributions and be able to share repository profiles such as GitHub or GitLab as part of their application.
• No specific visa or work-authorization details are provided in this listing, candidates must ensure they are eligible to work as contractors from their location.