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.

Desired Candidate Profile

• 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.