Python Backend Engineer for AI Agent Development

You will build and validate Python backend applications that replicate familiar software tools and support AI agent development. The work combines backend engineering, structured quality checks, and the creation of data used to train and evaluate AI agents.

You may also study workflows, turn them into realistic long-horizon tasks, and write rubrics that show whether an agent's work is correct, partly correct, or deficient. AI coding assistants are used throughout development and validation.

• Build backend connectors for tools such as Slack, Linear, Jira, Notion, Gmail, and internal wikis.
• Test connector behavior, extend existing connectors, and create new connectors against agreed milestones.
• Identify workflows that can become long-horizon AI agent tasks.
• Write realistic tasks and verify their quality and correctness through structured quality assurance.
• Create evaluation rubrics for correct, partially correct, and deficient work.
• Take part in calibration and quality reviews while meeting the required quality standard.

What it pays and takes

This is a remote contractor engagement scheduled for four weeks and starting immediately. The role is listed as entry level, while the required technical background includes at least three years of backend development experience.

• $300 per approved task.
• At least one approved task must be completed each day.
• No stated cap on the number of tasks completed while work is available.
• At least 20 hours per week.
• Remote and open worldwide.
• Fluency in English.
• Strong Python skills with FastAPI, Flask, or Django.
• Experience building scalable backends, REST APIs, and microservices.
• Working knowledge of Git, Docker, pipelines, testing, and code-quality practices.
• Daily experience using AI coding assistants such as Claude Code, Codex, Cursor, GitHub Copilot, or similar tools.

How it works

Apply on OpenTrain with your resume, then complete the application on the hiring site.

About AI training work

AI training work uses human-built examples, reviews, and evaluations to help artificial intelligence systems learn how to perform useful tasks. Experienced contributors are paid for applying skills such as software development, quality assurance, task design, and evaluation to this process.