Evaluation Scenario Writer
Pubblicato il 18-09-2026 - Altro in Firenze
At Mindrift, innovation meets opportunity. We believe in using the power of collective human intelligence to ethically shape the future of AI. What We Do
The Mindrift platform connects specialists with AI projects from major tech innovators. Our mission is to unlock the potential of Generative AI by tapping into real‐world expertise from across the globe.
About The Role
Design realistic and structured evaluation scenarios for LLM‐based agents. Create test cases that simulate human‐performed tasks and define gold‐standard behavior to compare agent actions against. Ensure each scenario is clearly defined, well‐scored, easy to execute, and reusable. Require sharp analytical mindset, attention to detail, and interest in how AI agents make decisions.
Create structured test cases that simulate complex human workflows.
Define gold‐standard behavior and scoring logic to evaluate agent actions.
Analyze agent logs, failure modes, and decision paths.
Work with code repositories and test frameworks to validate scenarios.
Iterate on prompts, instructions,
and test cases to improve clarity and difficulty.
Ensure scenarios are production‐ready, easy to run, and reusable.
How To Get Started
Apply to this post, qualify, and get the chance to contribute to projects aligned with your skills, on your own schedule. From creating training prompts to refining model responses, you'll help shape the future of AI while ensuring technology benefits everyone.
Requirements
Bachelor's and/or Master's Degree in Computer Science, Software Engineering, Data Science / Data Analytics, Artificial Intelligence / Machine Learning, Computational Linguistics / NLP, Information Systems or related fields.
Background in QA, software testing, data analysis, or NLP annotation.
Good understanding of test design principles (e.g., reproducibility, coverage, edge cases).
Strong written communication skills in English.
Comfortable with structured formats like JSON/YAML for scenario description.
Can define expected