Most people who don't get in fail the screening, not the CV. Five minutes here first: How to pass the micro1 AI interview with Zara.
What the work is
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.
The platform posted this on 4 Nov 2026.