Open Source Software Engineer for AI Model Training

Create reproducible Reinforcement Learning environments that evaluate an AI model''s ability to complete complex software engineering workflows. You will design environment scenarios and provide golden reference solutions that exercise real-world DevOps, CI/CD, debugging, and multimedia toolchains using common CLI tools such as git, docker, gdb, asan, ffmpeg, and others. Contributions will be delivered to open-source repositories supporting a frontier AI training and evaluation program run by micro1.

• Create production-quality reinforcement learning environments that reproduce complex software engineering tasks and workflows.
• Author golden reference solutions and reproducible test scenarios that validate model performance.
• Contribute code, reviews, and documentation to open-source repositories tied to the project.
• Design, implement, and optimize algorithms and system components in one or more of these languages: C++, Python, Java, Go, TypeScript, or Rust.
• Investigate and fix technical challenges, bugs, and performance bottlenecks in existing codebases.
• Collaborate with other contributors and stakeholders to align deliverables with project goals and engineering best practices.
• Maintain and improve technical documentation to support knowledge sharing and onboarding.
• Participate in code discussions and provide constructive feedback during reviews.

• Demonstrable open source contributions and a public profile on GitHub, GitLab, or similar, that you can share as part of your application.
• Proficiency in one or more of: Python 3, Java, C++, Go, TypeScript, or Rust (Rust at a basics level is acceptable).
• Experience writing production-quality code, and contributing to code reviews and documentation.
• Preferred: previous work on large-scale or distributed codebases.
• Preferred: familiarity with modern AI or machine learning systems, though this is not required.

• Role type: Contractor, remote.
• Engagement is output-oriented, contributors are paid per task that meets project specifications.
• Minimum submission requirements apply. Experts must submit a minimum of tasks per week.
• Timing expectations: we typically fill roles within 48 hours, and selected contributors are expected to begin their first tasks within 24, 48 hours of completing onboarding.

• Pay range: $50 to $150 per hour, listed as an equivalent range. Actual pay is output-based and paid per completed task that satisfies project requirements.
• Time to complete tasks will vary by expert experience and workflow.

• Remote contributors worldwide may apply, subject to contractor engagement terms.
• Applicants must be able to provide verifiable open source work samples or profiles on platforms such as GitHub or GitLab.

• Apply to the role and answer screening questions through the application form.
• Complete an AI-mediated interview, approximately 30 minutes.
• Complete a technical assessment, tentative based on the role.
• Final review by the hiring manager.