Software Engineering Evaluation Specialist

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 - 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. 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