PROJECT/OPPORTUNITY DETAILS

A SWEAP task is a real-world GitHub problem packaged with a testing environment and solution (i.e., the golden patch). This environment and solution are used to train an agent to solve the problem and verify the solution in the testing environment. A contributor’s job is to verify the testing environment based on testing logs, categorize the problem by specificity and knowledge areas, and write additional notes to help an agent understand what a good solution looks like based on the code edited in “the golden patch” and notes on GitHub.

OVERVIEW

Join our expert annotation team to create training data for the world’s most advanced AI models. No previous AI experience is necessary. You’ll get your foot in the door with one of the most prominent players in the AI/LLM space today. We’re seeking contributors with professional software engineering experience on production repositories and experience building and maintaining large-scale coding repositories. Projects typically include discrete, highly variable problems that involve engaging with these models as they learn to code. We currently have 200+ roles open!

REQUIRED QUALIFICATIONS

• Professional software engineering experience on production repositories.
• Experience building and maintaining large-scale coding repositories.
• Complete fluency in the English language.
• Ability to articulate complex technical concepts clearly and engagingly.
• Excellent attention to detail and ability to maintain consistency in writing. Solid understanding of grammar, punctuation, and style guidelines.

Please refer the Job description for details