AI DATA ANNOTATOR
Scope: Training physical AI and humanoid robots to understand the real world using first-person (egocentric) videos of household chores and workflows. Labelling Taks: Marking precise start/end timestamps and writing highly detailed text tags for atomic actions. Project size: A global pipeline with continuous data submission and high volume annotation work. Quality Measures: Strict entry requirements (a 90% score threshold on the entry exam), continuous patformspot-checks, and a multi-pass peer review system by senior auditors.