Software Engineering Evaluation Specialist

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 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-r