Évaluateur de contenu visuel

Role Title: Visual Content Evaluator

Role Type: Contractor

Location: Remote

micro1 is engaging Visual Content Evaluators to support a customer initiative focused on enhancing the quality of AI-generated images. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required, your domain knowledge is what matters.

Scope of Work

• Upload original images you own that strictly follow provided project specifications.
• Compare each original image with its AI-generated counterpart, performing meticulous, side-by-side assessments.
• Identify and document all visual discrepancies, including subtle flaws in lighting, perspective, texture, anatomy, and color fidelity.
• Detect and flag common AI image artifacts such as warped hands, smudged surfaces, inconsistent shadows, broken patterns, or misshapen text.
• Compose clear, accurate, and concise written descriptions for each identified issue, pinpointing exact locations within the images.
• Consistently apply a high standard of quality assurance by following detailed project guidelines and prioritizing accuracy over speed.
• Maintain thorough, constructive feedback throughout your evaluations, ensuring actionable insights for model improvement.

Preferred Qualifications

• Based in the United States (non-negotiable project requirement).
• Exceptional written and verbal communication skills in English, producing error-free feedback and detailed image notes.
• Keen visual acuity with a proven ability to detect even subtle differences between similar images.
• Background in photography, digital imaging, graphic design, photo editing, or a closely related discipline, with the precise vocabulary to describe technical visual issues.
• Understanding of AI image generation processes and typical failure points.
• Demonstrated experience in image evaluation, data annotation, comparison, or similar visual quality assurance contexts.
• Reliable, detail-oriented approach with a commitment to careful, comprehensive review on every assignment.