joan jon

I've supported AI and computer vision projects at Hugo Technologies and Wonder AI Global by labeling, validating, and reviewing large-scale datasets across image, video, audio, text, traffic, and 3D data.

At Hugo Technologies, I annotated more than 50,000 data points at 98% accuracy, including video annotation, bounding-box segmentation, traffic and vehicle segmentation, 3D annotations, and photorealistic image labeling. I partnered with QA teams and data scientists to resolve edge cases, correct inconsistencies, and maintain dataset integrity.

At Wonder AI Global, I've labeled and reviewed over 10,000 image, video, and audio data points while maintaining 95% accuracy. Through detailed review, validation, audio segmentation, and transcription, I've reduced annotation errors by over 80%.

My background as an educator and ICT Officer strengthened my communication, record-management, analytical thinking, and ability to explain complex work clearly. I bring consistency, careful attention to guidelines, and a reliable approach to meeting quality targets and deadlines.