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 may apply during onboarding or non-core project phases. Payment details are shared per project.