Frontend Code Evaluation Specialist
Pubblicato il 04-10-2026 - Obsidian in Ferrara
About the work We're building a high-quality dataset of human preference judgments on AI-generated frontend code. Each task hands you a reference web page — crawled from the real internet, delivered as a full zipped site tree plus screenshots of its default view and, on some pages, additional states reached by hovering, clicking, or scrolling. Alongside it come two model attempts, A and B, each a zipped self-contained site tree. The models only ever saw the screenshots; they never had the source.
You download all three, run them locally, view each at a 1920×1080 viewport, interact with them to reach every required state, then open the source of both attempts and grade them against each other — on visual fidelity per state, and on how the code is actually constructed. Structure and responsiveness are explicitly part of the rubric, not just the render.
This is evaluation work, not authoring. The defining skill is not that you can build a page — it's that you can open someone else's page and tell how it was built and where it cheats.
Please read before applying
- Each unit takes roughly 2–3 hours and is timed. This is not microtask work; if you can only offer scattered 15-minute windows, you will not be able to finish a unit.
- You need a real local development environment. A tablet, a Chromebook, or a locked-down work machine that cannot run a local static server will not work for this project.
- You need at least one completed Mercor engagement, delivered in full. We are not onboarding net-new experts to this project.
What you'll do
- Render a reference page and two candidate replications at 1920×1080 and judge which is the closer reproduction, state by state.
- Diff visual fidelity in detail: box model and spacing, typography (family, size, weight, line-height, letter-spacing), color and border treatment, image and asset handling, z-order and overflow.
- Read the source of both attempts and grade construction quality — distinguishing a replication that is genuinely