Freelance Software Engineer - AI Coding Agent Evaluation

Please submit your CV in English and indicate your level of English proficiency.

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 an 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

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3+ years of production software development in one backend stack — Python, Go, Node.js, Java, or Rust. Depth in one stack beats breadth.

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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.

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Docker authoring — reproducible Dockerfiles, pinned dependencies, multi-stage builds when needed, non-root user.

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Linux & Bash — comfort debugging inside containers (strace, lsof, journalctl); shell beyond set -euo pipefail.

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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.

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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.

Process

Apply → Pass qualification (90-minute sample-task screen + short behavioral interview) → Join a project → Complete tasks → Get paid.
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 m