Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems.
Participation is project-based, not permanent employment.
About the Role
You’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.
Responsibilities
- 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.
- Write a instruction.
- md that reads like a Jira ticket a developer would receive.
- Write a reference solve.
- sh proving the task is solvable.
- 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.
- Not in scope Data labeling, prompt engineering.
- Production code to ship — you design problems and verification for AI agents.
- Leetcode puzzles — scenarios must look like real developer work.
- Not every candidate task ships — quality over quantity.
Requirements
- 3+ years of production software development in one backend stack — Python, Go, Node.
- js, Java, or Rust.
- Depth in one stack beats breadth.
- Python + pytest fluency — required regardless of primary stack.
- The task harness is pytest-based even when the broken app is in another language.
- Fixtures, parametrize, monkeypatch, timeouts, conftest.
- py. 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.
- You can cite a specific time the AI was confidently wrong and how you caught it.
- English — B2+ written.
- Not a fit Data Science, ML, or Computer Vision engineers without backend-engineering output.
- Manual QA testers without automation or test authoring.
- Frontend-only, low-code / no-code, IT Support, or Business Analysts.
- Engineers who have never written pytest from scratch.
- Junior, intern, or assistant as the most recent role.
Preferred qualifications
- Domain depth in Security, System Administration (nginx / systemd / cron), Scientific Computing (NumPy / PyTorch / SciPy), DevOps, or Git internals.
- Modern Python tooling (uv, poetry, pyproject.
- toml). Coverage tooling (pytest-cov, coverage.
- py, gcov, llvm-cov, kcov).
- Fuzzing or property-based testing (Hypothesis).
- Prior contribution to agent-evaluation benchmarks or related frameworks.
Time commitment
Onboarding: ~10 hours per first task.
Steady state: ~5 hours per task, 2–4 parallel tasks per author.
Realistic weekly load: 8–20 hours.
Higher volume available for top performers.
You choose when and how to contribute; tasks must be submitted by the deadline and meet acceptance criteria.
Compensation
- Paid contributions, rates up to $35/hour *.
- Task-based compensation equivalent to hourly rate, depending on performance and volume.
- Some projects include incentive payments.
- *Rates vary based on expertise, skills assessment, location, project needs, and other factors.
- Higher rates may be provided to highly specialized experts.
- Lower rates may apply during onboarding or non-core project phases.
- Payment details are shared per project.