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# Data Labeling Analyst

- **Company:** TUNDRA TECHNICAL
- **Location:** White Plains, NY, United States
- **Contract:** Temporary to permanent
- **Skills:** Artificial Intelligence, Big Data, Human-Computer Interaction
- **Published:** October 20, 2026
- **Apply:** https://www.juju.com/job/23_3D076A3AEB9A7CD873F0F4000D40AE6D

## About the Role

Minimum qualifications: * Graphic design portfolio required as part of job application. * Understanding of visual design principles, including hierarchy, contrast, balance, proximity, alignment, repetition, and unity. * Exceptional attention to detail and the ability to critically assess visual content. * Ability to identify trends within large data sets and provide actionable insights. * Excellent verbal and written communication skills to convey feedback and collaborate with Meta AI teams. * Bachelor's of Fine Arts (BFA) in Graphic Design. * Proven experience in graphic design, visual arts, social media creative, marketing ad creative or a related discipline.
Preferred qualifications * Knowledge of user experience (UX) and user interface (UI). * Master's degree or higher in a design-related field.

## Description

Description Job Title: Design Data Labeling Analyst Summary: As a member of our design annotation team, this role will be responsible for ensuring the compositional quality of AI-generated images for advertisers.
The ideal candidate for this role will have a background in graphic design and the ability to provide detailed feedback on the visual elements of images, ensuring that they meet specific standards of quality and aesthetic appeal.
Responsibilities: * Evaluate AI-generated images to determine compliance with established visual design principles. * Annotate and provide detailed feedback on images, highlighting areas of success and areas for improvement. * Maintain a consistent and high standard of evaluation across a diverse range of images. * Analyze large data sets to inform product decisions, and communicate with Product Design leads to pinpoint trends in model performance.