Python Backend Engineer for AI Agent Data

You will support AI agent training and evaluation through backend development, workflow design, and structured quality review. The work has two related tracks: building connectors that reproduce SaaS tools, and creating realistic tasks and rubrics to judge the quality of completed work.

You will use AI coding assistants during development, testing, and validation, and take part in onboarding, calibration, and quality reviews.

• Build Python backend applications that reproduce tools such as Slack, Linear, Jira, Notion, Gmail, and internal wikis.
• Test connectors for faithful behavior, extend existing integrations, and deliver new connectors against agreed milestones.
• Turn real workflows into realistic, long-horizon software tasks.
• Create rubrics that distinguish correct, partly correct, and deficient work.
• Assess task quality, realism, and correctness.

What it pays and takes

This is a remote contractor engagement lasting four weeks and starting immediately. The role requires at least one completed and approved task each day, with additional tasks available while the project pipeline remains active.

• Pay: $300 per approved task.
• Time: At least 20 hours per week.
• Location: Remote worldwide.
• Language: English.
• Work type: Contractor and part time.
• Experience: At least three years of backend development experience with strong Python skills.
• Backend frameworks: Experience with FastAPI, Flask, or Django.
• Technical skills: Scalable backends, REST APIs, microservices, Git, Docker, pipelines, testing, and code-quality practices.
• AI tools: Daily experience with AI coding assistants such as Claude Code, Codex, Cursor, or GitHub Copilot.
• Helpful background: SaaS integrations, turning real workflows into software tasks, or creating structured evaluation criteria.

How it works

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

About AI training work

AI training work is the human side of building artificial intelligence. People write code, create tasks, and review results so AI systems can learn to complete useful work more accurately; specialized experience is valuable because it supports careful, reliable evaluation.