UI/UX Design Expert - Evaluator - AI Trainer
Obsidian
1. Role Overview Mercor is partnering with a leading AI research organization to engage experienced UI/UX and product designers for a project focused on evaluating how well AI systems perform real-world digital product design work. Rather than producing deliverables yourself, you will define what excellent work looks like: designing task-specific grading criteria and scoring completed work samples with rigorous, well-reasoned written justifications. 2. Key Responsibilities Design precise, task-specific grading criteria for real-world digital product and UX deliverables (design systems and component libraries, responsive UI layouts and dashboards, end-to-end product flows such as onboarding, checkout, and booking, logged-in customer portals, UX writing and product content, and design-to-engineering/production handoff) Score AI-generated and human work samples against those criteria, with detailed written justifications for every score Apply consistent, evidence-based judgment so that scores are reproducible and defensible Incorporate structured feedback from senior reviewers and iterate quickly on your work 3. Ideal Qualifications 5+ years of professional experience in product design, UI/UX, or design systems for real digital products (not brand or marketing sites) Background at leading digital-product and UX studios, in-house product-design teams at major product-led companies, or e-commerce and marketplace companies with sophisticated customer portals and dashboards Experience across one or more relevant tracks: Product / UI / Design Systems Designer, UX Lead, UX Writer / Content Designer, or digital Product / Business Analyst Deep fluency in the day-to-day craft: interaction and visual design, design systems, responsive layouts, end-to-end product flows, prototyping and production handoff in Figma, and articulating critique the way a design or UX lead would in a review Exceptionally strong written communication, with the ability to explain exactly why a piece of work does or does not meet the bar for a real product and its users Detail-oriented, consistent, and comfortable having your judgment reviewed and calibrated against peers Prior experience with AI training, evaluation, or human-data projects is a strong plus #J-18808-Ljbffr Obsidian
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