Software Engineering Evaluation Specialist

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.

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.

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

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

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

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

Domain depth in Security, System Administration (nginx / systemd / cron), Scientific Computing (Num Py / PyTorch / Sci Py), Dev Ops, 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

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

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

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