Open Source Software Engineer for AI Model Training

Build 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 reflect real-world DevOps, CI/CD, debugging, and media processing tasks using common command line tools such as git, docker, gdb, asan, ffmpeg, and others.

• Create reproducible RL environments that test models on multi-step software engineering workflows.
• Produce a golden reference solution for each environment, demonstrating the expected correct behavior.
• Contribute production-quality code, pull request 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, GoLang, Typescript, or Rust.
• Identify and fix technical issues, bugs, and performance bottlenecks in existing codebases.
• Collaborate with other contributors and stakeholders to align on deliverables and best practices.
• Create and maintain technical documentation to support knowledge sharing and onboarding.
• Participate in code discussions and provide constructive review feedback to raise overall code quality.

• Demonstrable open source contributions and an active profile on GitHub, GitLab, or similar platforms. Applicants must have clear open source contributions and profiles to showcase it.
• Proficiency in one or more of the following languages: Python3, JAVA, Rust (basics acceptable), C++, Typescript, GoLang.
• Experience writing production-quality code, performing code reviews, and producing technical documentation.
• Preferred: previous work on large-scale distributed codebases.
• Preferred but not required: familiarity with modern AI or machine learning systems.

• Role type, remote: Contractor, fully remote.
• Engagement requires contributing to open-source repositories relevant to the customer project.
• 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.
• Output expectations: Compensation is output-based and experts are paid per task that meets project specifications. Minimum submission requirements apply. Experts must submit a minimum of tasks per week.

• Listed hourly range: $50 to $150 per hour.
• Actual pay is structured per-task based on meeting project specifications, with time to complete varying by expert experience and workflow.

• This engagement is open to contributors who can work remotely as contractors.
• Applicants must be able to demonstrate prior open source work via public repositories or contributor profiles (for example GitHub or GitLab).

• Apply to the role and complete the screening questions.
• Complete an AI interview, approximately 30 minutes in length.
• Complete a technical assessment (tentative) for evaluation.
• Final review by a hiring manager.