Software Engineering Evaluation Specialist

🏢 Mindrift
📍 KuwaitPart-timeRemote
📅 Posted: 3d ago🔄 Updated: 3d ago
CV%
✨ AI Summary
Mindrift is seeking a Software Engineering Evaluation Specialist to design and evaluate coding tasks for AI agents. The role involves creating realistic developer scenarios, building Docker environments, writing pytest tests, and developing reference solutions. The ideal candidate has 3+ years of production software development experience in a backend stack, fluency in Python and pytest, and experience with Docker, Linux, and AI coding agents. This is a project-based role with flexible hours and compensation based on contributions, up to $35/hour.
Required Skills
Information Technology
PythonPyTestDockerLinuxDebugging
Other
BashAI coding agent
Nice to have:
Information Technology
Cloud SecuritySystems AdministrationDevOpsGitPython
Science & Research
Scientific Computing
Engineering, Construction & Trades
Tooling
Other
Fuzzingproperty-based testing
Finance, Legal & Governance
Valuation
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 RoleYou’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 scopeData 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.Requirements3+ 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 fitData 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 qualificationsDomain 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.ProcessApply → Pass qualification (90-minute sample-task screen + short behavioral interview) → Join a project → Complete tasks → Get paid.Time commitmentOnboarding: ~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.ApplySubmit your CV via the Mindrift platform. Indicate your English level, note this role (Software Engineering Evaluation Specialist — Terminal Bench), and include a GitHub profile link if available.
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🎯 Overalli74%
⚡ Skillsi85%
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Ontology Match: 85.0
Matched:✓ Requirements Matching✓ Ontology Skills Mapping
📜 Eligibilityi49%
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Local: 19600%
🏗️ Career Fiti91%
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Seniority: 91.0
📋 Requirementsi67%
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Domain: 67.0
🔥 Motivationi78%
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Title Fit: 78.00