Software Engineer - (Python + pytest) AI Coding Agent Evaluation - Freelance/Remote 100+ openings

Description

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. 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.

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