TURING
The Mission I worked on high-volume data labelling aimed at sharpening model accuracy for annotation and labelling. The goal was simple: transform raw, messy data into a precise "source of truth" that the AI could actually learn from. The Work I handled complex annotation tasks This wasn't just basic clicking; it required deep focus on pixel-perfect masking, tracking objects across video frames, and identifying specific behaviors or attributes that a standard algorithm might miss. The Scale & Standard Size: Managed a massive dataset of hundreds of thousands units, delivering consistent results across a long-term timeline. Quality: I didn't just aim for "good enough." By using a mix of peer reviews and automated checks, I maintained a 90% accuracy rate. Reliability: Even with "edge cases the weird, blurry, or confusing data points I ensured every label met strict project guidelines so the model wouldn't pick up bad habits. Quick Highlights Focus: High-precision 2D/3D annotation and segmentation. Volume: Successfully processed over 3000 assets.