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# AI Evaluation Engineer (Python, QA or Security)

- **Company:** Mindrift Alle
- **Location:** München, Germany
- **Experience:** Expert
- **Salary:** €104,000.0
- **Contract:** Permanent contract
- **Skills:** JavaScript (Programming Language), Artificial Intelligence, Python (Programming Language), PostgreSQL, Redis, Software Engineering, TypeScript, ReactJS, Prompt Engineering, Fastapi, Apache Kafka, Codebase, Virtual Agents, Software Coding, Docker
- **Published:** August 6, 2026
- **Apply:** https://www.careerjet.de/jobad/dee3d87b23313d611f05079c4cd7b6f522

## About the Role

* 5+ years in software development
 * Core stack: Python (FastAPI), JavaScript/TypeScript (React), Docker, Postgres, Kafka, Redis
 * Experience writing tests (functional, integration)
 * English proficiency - B2+

## 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.
What this opportunity involves We're building a dataset to evaluate AI coding agents - how well a model handles real-world developer tasks.
You'll create challenging tasks and evaluation criteria within realistic simulated environments:

 * Build realistic developer environments - a virtual company with codebase, infrastructure, and context (tickets, docs, conversations) that forms a believable development history
 * Design tasks from intermediate states of these environments - craft the prompt, define what "solved" means, and ensure the task is solvable by an AI agent
 * Write tests that verify agent solutions - accept all valid approaches and reject incorrect ones, neither too strict nor too lenient
 * Iterate on tasks and tests based on QA feedback - review agent solutions, analyze failures, and refine until the evaluation is fair and robust

What this is NOT

 * Not data labeling
 * Not prompt engineering
 * Not writing code from scratch - the agent writes most of the code; you guide and evaluate, Frontier models are already good at coding. Creating a task that genuinely challenges the best models is non-trivial. You need to deeply understand where models fail and what scenarios reveal the difference between a good and a bad solution. Tasks have many valid solutions - writing tests that accept all correct solutions and reject incorrect ones is harder than it sounds.
How it works
Apply * Pass qualification(s) * Join a project * Complete tasks * Get paid


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