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# Software Engineering Evaluation Specialist

- **Company:** Búsqueda Avanzada
- **Location:** Valencia, Spain
- **Salary:** €93,600.0 - €166,400.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Artificial Intelligence, JIRA, Bash Shell, Cron, Extract Transform Load (ETL), Cursor (Graphical User Interface Elements), Software Debugging, Linux, DevOps, Python (Programming Language), Nginx, Node.Js, NumPy, Scientific Computating, SciPy, Software Engineering, Pytorch, Prompt Engineering, Backend, Git, Pytest, Production Code, Docker, Golang
- **Published:** August 18, 2026
- **Apply:** https://www.adzuna.es/contact-us.html

## About the Role

+ 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., + Domain depth in Security, System Administration (nginx / systemd / cron), Scientific Computing (NumPy / PyTorch / SciPy), DevOps, or Git internals.

## Description

+ 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., Apply * Pass qualification (90-minute sample-task screen + short behavioral interview) * Join a project * Complete tasks * Get paid.
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.


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