TL;DR

AI Visual Quality Annotation Specialist (AI): Helping train, evaluate, and improve AI-powered creative tools by applying expert annotation, quality assessment, and design-led thinking with an accent on visual evaluation, design quality assessment, and cross-functional collaboration. Focus on identifying patterns, errors, and edge cases to improve model performance and translating design concepts into structured feedback for model teams.

Company

%hirify_global% is seeking an AI-Native Visual Designer to shape AI systems and ensure models deliver visually excellent, brand-aligned, and human-centred output.

What you will do

Annotate and label large volumes of visual data (images, UI layouts, compositions, graphics, photos).
Evaluate AI-generated output for accuracy, aesthetic quality, usability, and alignment with design principles.
Develop visual scoring criteria and rubrics to guide model improvement across various creative formats.
Collaborate with machine learning engineers, research scientists, and product teams to provide design-driven feedback loops.
Use generative AI tools to create visuals, test hypotheses, and prototype user workflows.
Maintain data quality and consistency in annotation projects, contributing to taxonomy development.

Requirements

Strong grasp of composition, color theory, typography, layout, visual hierarchy, and storytelling principles.
Portfolio demonstrating visual design excellence across one or more domains.
Familiarity with generative AI tools (image models, large language models, design AIs).
Understanding of how AI training data, annotation, and quality metrics influence model behavior.
Ability to evaluate model output with precision and articulate actionable feedback.
Able to translate design critique into structured, repeatable feedback for technical and product partners.

Nice to have

Experience with Figma, Adobe Creative Cloud, or 3D tools.
Prior data labeling, annotation, or ML operations experience.
Basic familiarity with scripting (Python), prompt engineering, or dataset tooling.
Knowledge of design accessibility (WCAG), usability heuristics, or user research methods.

Culture & Benefits

Curious about the future of AI-driven creativity.
Comfortable with both artistic judgment and structured evaluation.
Excited to influence how AI models “see” and “create”.
Skilled in both visual taste and systematic thinking.