Software Engineering Evaluation Specialist

• 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., * 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., 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.

• 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., * 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.

• Paid contributions, rates up to $35/hour*.
• Task-based compensation equivalent to hourly rate, depending on performance and volume.
• Some projects include incentive payments.

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.