AI Agent Evaluation Analyst (m/f/d)

We’re on the hunt for QAs for autonomous AI agents for a new project focused on validating and improving complex task structures, policy logic, and agent evaluation frameworks. Throughout the project, you’ll have to balance quality assurance, research, and logical problem-solving. This project opportunity is ideal for people who enjoy looking at systems holistically and thinking through scenarios, implications, and edge cases.

You do not need a coding background, but you must be curious, intellectually rigorous, and capable of evaluating the soundness and consistency of complex setups. If you’ve ever excelled in things like consulting, CHGK, Olympiads, case solving, or systems thinking — you might be a great fit.

What you’ll be doing:

• Reviewing evaluation tasks and scenarios for logic, completeness, and realism.
• Identifying inconsistencies, missing assumptions, or unclear decision points.
• Helping define clear expected behaviors (gold standards) for AI agents.
• Annotating cause-effect relationships, reasoning paths, and plausible alternatives.
• Thinking through complex systems and policies as a human would to ensure agents are tested properly.
• Working closely with QA, writers, or developers to suggest refinements or edge case coverage.

• Excellent analytical thinking: Can reason about complex systems, scenarios, and logical implications.
• Strong attention to detail: Can spot contradictions, ambiguities, and vague requirements.
• Familiarity with structured data formats: Can read, not necessarily write JSON/YAML.
• Can assess scenarios holistically: What's missing, what’s unrealistic, what might break?
• Good communication and clear writing (in English) to document your findings.

We also value applicants who have:

• Experience with policy evaluation, logic puzzles, case studies, or structured scenario design.
• Background in consulting, academia, olympiads (e.g. logic/math/informatics), or research.
• Exposure to LLMs, prompt engineering, or AI-generated content.
• Familiarity with QA or test-case thinking (edge cases, failure modes, “what could go wrong”). Some understanding of how scoring or evaluation works in agent testing (precision, coverage, etc.).