Freelance Video Data Annotator (Turing, Remote)
Reviewed first-person (egocentric) video clips at precise timestamps to support computer vision and vision-language model dataset creation. Produced evidence-based, frame-relevant visual annotations strictly grounded in visible content to generate high-quality ground truth. Audited and cross-checked that questions, visual evidence, and responses matched the project’s granular formatting guidelines. • Timestamp-accurate egocentric video analysis • Visual evidence selection for frame-by-frame labeling • Bias mitigation by avoiding leakage from future frames or external assumptions • High-volume QA via alignment checks against granular specs