Role OverviewYou'll design coding tasks that challenge frontier AI coding agents. Each task is a self-contained Docker environment with a broken piece of software; an AI agent attempts the fix; automated tests verify the outcome. Your deliverable is the full task package: broken code, tests, instructions, and a reference solution proving the task is solvable.
What You Will Do
Invent a realistic developer scenario — a real bug, a broken ETL, a missing feature — not a toy problem. Build a reproducible Docker environment with pinned dependencies. Write a pytest that verifies outcomes, not specific commands — deterministic, non-flaky, and does not leak the fix.
Why It Might Be a Fit
Calibrate difficulty so current state-of-the-art agents solve the task 20–60% of the time. Iterate based on feedback from expert QA reviewers. Later: review other authors’ tasks as a QA reviewer.
Requirements
- 3+ years of production software development in one backend stack — Python, Go, Node.js, Java, or Rust.
- Python + pytest fluency — required regardless of primary stack.
- Docker authoring — reproducible Dockerfiles, pinned dependencies, multi-stage builds when needed, non-root user.
- Linux & Bash — comfort debugging inside containers (strace, lsof, journalctl); shell beyond set -euo pipefail.
- AI coding agent experience — Claude Code, Cursor, Roo Code, or similar, on non-trivial work.
- English — B2+ written.
Benefits
- Paid contributions, rates up to $35/hour*.
- Task-based compensation equivalent to hourly rate, depending on performance and volume.
- Some projects include incentive payments.
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