Software Engineering Evaluation Specialist

Mindrift; Dubai; On-site; AED 126,478,000 - 177,069,000; Full time

Job summary

Mindrift connects specialists with project-based AI opportunities. You will design realistic coding tasks, create self-contained Docker environments, and produce tests that verify outcomes without revealing fixes. Tasks are reviewed by QA and iterated based on feedback, ensuring quality and solvability.

The role emphasizes backend-focused development, Python/pytest fluency, and Docker expertise, with project-based compensation and non-permanent engagement.

Qualifications

• 3+ years of production software development in one backend stack (Python, Go, Node.js, Java, or Rust).
• Python + pytest fluency required; the task harness is pytest-based even if the app is in another language.
• Docker authoring with reproducible environments, pinned dependencies, and non-root user.
• Linux and Bash proficiency for debugging containers (strace, lsof, journalctl).
• AI coding agent experience with Claude Code, Cursor, Roo Code, or similar, on non-trivial work.
• English – B2+ written.

Responsibilities

• Invent a realistic developer scenario (real bug, broken ETL, missing feature).
• Build a reproducible Docker environment with pinned dependencies.
• Write a pytest that verifies outcomes (deterministic, non-flaky).
• Create an instruction.md that reads like a Jira ticket.
• Provide a reference solve.sh proving the task is solvable.
• Calibrate difficulty so state-of-the-art agents solve 20–60% of tasks.
• Iterate based on QA feedback; review other authors’ tasks as QA reviewer.

Skills

Python

pytest

Docker

Linux & Bash

English (B2+)

Backend development

Tools

Docker

pytest

Job description

Job Description:

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

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.

Requirements: