Mindrift
Software Engineering Evaluation Specialist
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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
pytestthat verifies outcomes, not specific commands — deterministic, non-flaky, and does not leak the fix. - Write an
instruction.mdthat reads like a Jira ticket a developer would receive. - Write a
solve.shproving 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.
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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 beyondset -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.
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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 may apply during onboarding or non-core project phases. Payment details are shared per project.
Apply
Submit your CV via the Mindrift platform. Indicate your English level, note this role (Software Engineering Evaluation Specialist — Terminal Bench), and include a GitHub profile link if available.
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