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Graphic Design Expert (International)

HumanSignal
Desde casa

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Información del empleo

Medio tiempo

Cualificaciones

Modelos de inteligencia artificial
Imagen
Diseño gráfico
Garantía de calidad
Capacitación
Diseño gráfico
UX
WCAG
Sketch
Recolección de datos
Polaco
Experiencia en enseñanza

Descripción completa del empleo

About HumanSignal

Real-world data is the competitive edge in AI.

HumanSignal is a human data partner for companies building AI models and products. Our customers ship better AI, faster, because we partner with their researchers from real-world data creation to annotation to delivery.

We design and create datasets from scratch, recruit and manage the domain experts who evaluate model output, and run everything through our own platform, Label Studio, the open-source standard for data labeling and evaluation, used by over 1 million practitioners worldwide.

We specialize in the operationally complex: real-world data collection, multimodal pipelines, and multi-step workflows. Advanced ML and AI teams use our enterprise platform to run their own data factories, and our services team to extend their reach where in-house capacity runs out.

If you want to do work that materially shapes how the next generation of AI products gets built, we'd love to talk.

Graphic Design Expert

Location: Remote
Type: Part-Time Contract (15+ hours per week for 2 weeks | 30 hour minimum commitment)
Duration: 2 weeks with potential for ongoing work
Compensation: Up to $20/hour for US based candidates

About the Project

You'll evaluate and rate graphic design elements on standardized quality scales to train AI models that assess design effectiveness—contributing to cutting-edge technology that advances how AI understands and evaluates visual content quality.

What You'll Do

Review and rate graphic design elements using our Label Studio Enterprise platform across multiple quality dimensions:
- Typography quality (readability, hierarchy, font pairing appropriateness)
- Visual hierarchy effectiveness (clarity, balance, focal point strength)
- Design element execution (icon quality, illustration polish, image selection)
- Color palette effectiveness (harmony, contrast, accessibility)
- Layout and composition quality (grid usage, spacing, alignment)
- Overall design effectiveness and professional polish
Apply consistent 1-5 rating scales across hundreds of design examples
Provide clear, objective assessments based on established design principles
Follow detailed rating guidelines and rubrics to maintain consistency
Process high volumes of design samples with sustained attention to detail
Participate in weekly calibration sessions to align rating standards
Meet quality benchmarks for rating consistency and inter-rater reliability

Basic Qualifications

Required Experience

Formal training in graphic design, visual arts, or related field (degree, certificate, or bootcamp completion) OR 2-3 years of professional graphic design experience
Strong proficiency in design principles (composition, color theory, typography, visual hierarchy)
Ability to objectively critique and evaluate design work against industry standards
Understanding of what constitutes effective vs. ineffective design choices
Experience reviewing or providing design feedback
Exceptional attention to detail and ability to maintain focus during repetitive tasks

Skills & Attributes

Comfort with highly repetitive work—ability to maintain quality and engagement while rating similar content for extended periods
Strong visual discrimination and consistent aesthetic judgment
Reliability and consistency in applying rating standards
Self-motivated with ability to work independently on repetitive tasks
Excellent focus and concentration for sustained periods
Strong communication skills for flagging edge cases or unclear examples
Comfortable with feedback and recalibration
Minimum 10 hours per week availability for at least 3 months

Nice-to-Haves

Preferred Background

Portfolio demonstrating diverse design work and critical eye
Experience as a design reviewer, art director, or in quality assurance roles
Understanding of accessibility standards (WCAG, inclusive design principles)
Knowledge of design across multiple contexts (branding, editorial, digital, UX/UI)
Experience teaching or mentoring design concepts

Technical Experience

Experience with Figma, Sketch, or other modern design tools
Prior data annotation, content moderation, or quality evaluation experience
Exposure to machine learning or computer vision concepts
Familiarity with design systems and component libraries

Work Style

High tolerance for repetitive evaluation tasks—finds rhythm and satisfaction in systematic work
Ability to maintain consistent standards across thousands of ratings
Strong pattern recognition—quickly identifying quality patterns across examples
Disciplined, methodical approach with minimal variation in judgment
Problem-solving mindset for handling ambiguous or borderline cases
Interest in AI/ML applications in design and creative fields
Previous remote work experience with self-directed tasks

What Makes Someone Successful in This Role

You thrive on consistency and repetition - You find satisfaction in systematic work and can maintain quality standards while evaluating similar content repeatedly without losing focus or accuracy.

You have a calibrated design eye - You can quickly assess design quality against objective criteria and apply the same standards consistently across hundreds or thousands of examples.

You're objective and principled - You separate personal taste from professional assessment, basing ratings on established design principles rather than subjective preference.

You maintain sustained focus - You can work through high volumes of repetitive tasks while staying sharp and attentive to subtle quality differences.

You're a clear communicator - You flag confusing examples or rating criteria early, ask clarifying questions, and document edge cases clearly.

You're curious about technology - You're genuinely interested in how AI learns to evaluate design quality and see value in contributing to machine learning training data.

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