Python Software Engineer for AI Agent Evaluation

You will work on one of two tracks. You may build Python backend connectors that replicate tools such as Slack, Linear, Jira, Notion, Gmail, and wikis, or create and evaluate realistic long-horizon tasks for AI agents.

Connector work includes testing faithful behavior, improving existing integrations, and delivering new connectors on schedule. Task evaluation includes finding useful workflows, writing realistic tasks, checking quality and correctness, and creating rubrics that separate correct, partly correct, and deficient work.

You will use AI coding assistants during development, testing, quality checks, and validation. The work also includes calibration and quality reviews.

• Build and validate Python connectors for SaaS tools.
• Extend existing integrations and deliver new connectors.
• Create realistic long-horizon tasks for AI agents.
• Write evaluation rubrics and perform structured quality checks.
• Use AI coding assistants such as Claude Code, Codex, Cursor, or GitHub Copilot.

What It Pays And Takes

This is a remote contractor engagement lasting four weeks and starting immediately. Work is available part time, with a minimum weekly commitment and a daily task requirement.

• Pay: $300 per approved task.
• Schedule: At least 20 hours per week.
• Daily requirement: Complete at least one approved task each day.
• Duration: Four weeks, starting immediately.
• Location: Remote and open worldwide.
• Availability: No stated cap on tasks while work remains available.
• Experience: At least three years of backend development experience.
• Technical skills: Strong Python, FastAPI, Flask, or Django experience.
• Backend knowledge: Scalable backends, REST APIs, and microservices.
• Tools and practices: Git, Docker, pipelines, testing, and code-quality practices.
• Language: Professional fluency in English.
• Work level: Entry level.

How It Works

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

About AI Training Work

AI training is the human work behind systems that learn from examples, including code, written responses, and task evaluations. People with software and engineering experience help make these examples accurate and useful, which is why specialist AI training projects seek strong technical skills.