Software Engineering Evaluation Specialist

🏢 Mindrift
📍 KuwaitPart-timeRemote
📅 Posted: 1w ago🔄 Updated: 1w ago
CV%
✨ AI Summary
Mindrift is seeking a Software Engineering Evaluation Specialist to design and create coding tasks for AI agents to test and improve AI systems. The role involves inventing realistic developer scenarios, building reproducible Docker environments, writing pytest for verification, and creating clear instructions and reference solutions. The ideal candidate has 3+ years of production backend software development experience, is fluent in Python and pytest, and proficient with Docker and Linux/Bash. Experience with AI coding agents is also required, along with B2+ written English proficiency. Preferred qualifications include domain depth in security, system administration, scientific computing, DevOps, or Git internals, as well as experience with modern Python tooling and coverage/fuzzing tools. This is a project-based role with flexible hours, offering compensation up to $35/hour.
Required Skills
Information Technology
PythonPyTestDocker
Other
Bashstracelsofjournalctl
Nice to have:
Information Technology
Cloud SecuritySystems AdministrationNginxNumPyPyTorchSciPyDevOpsGitPyTestSoftware Engineering
Soft Skills & Professional Competencies
Systems Thinking
Other
cronuvpoetrypyproject.tomlcoverage.pyllvm-covkcovFuzzingHypothesis
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