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.
- Up-to-date 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.

Disclaimer: This job posting & Location has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Software Engineer - AI Coding Agent Evaluation (India)
🏢 Mindrift
📍 India