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.

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.

Contemporary 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.

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📌 Freelance Software Engineer - AI Coding Agent Evaluation Mindrift · Remote Pay not listed → (Winnipeg)
🏢 Dorado
📍 Winnipeg