Evaluation Scenario Writer

09 set - Genova
Mindrift

3 days ago Be among the first 25 applicants

This opportunity is only for candidates currently residing in the specified country. Your location may affect eligibility and rates. Please submit your resume in English and indicate your level of English.
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, launched and powered by Toloka, connects domain experts with cutting-edge AI projects from innovative tech clients. Our mission is to unlock the potential of GenAI by tapping into real-world expertise from across the globe.

About The Role

We're looking for someone who can design realistic and structured evaluation scenarios for LLM-based agents. You'll create test cases that simulate human-performed tasks and define gold‑standard behavior to compare agent actions against. You'll work to ensure each scenario is clearly defined, well‑scored, and easy to execute and reuse. You'll need a sharp analytical mindset, attention to detail,



and an interest in how AI agents make decisions.

Responsibilities
- Design structured test scenarios based on real‑world tasks
- Define the golden path and acceptable agent behavior
- Annotate task steps, expected outputs, and edge cases
- Work with devs to test your scenarios and improve clarity
- Review agent outputs and adapt tests accordingly

How To Get Started

Simply 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 / Natural Language Processing (NLP), Information Systems or other related fields.
- Background i

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