AI Data Labeler

What You'll Do:

• Label images and video frames with bounding boxes, polygons, segmentation masks, and SKU-level classifications.
• Annotate edge cases including occlusion, overlapping items, glare, motion blur, and unusual product orientations.
• Maintain consistency against the labeling taxonomy and follow detailed annotation guidelines.

Quality Assurance:

• Compare model predictions to ground-truth labels and document failure modes.
• Audit annotations from peers and contractors to enforce inter-annotator agreement.
• Flag systemic issues such as recurring misclassifications, mislabeled SKUs, or low-quality captures.

Hardware & Process:

• Identify capture issues that indicate hardware problems like blurry frames, poor lighting, or camera misalignment.
• Test devices in lab and field conditions to confirm image quality and end-to-end checkout accuracy.
• Maintain and refine internal labeling guidelines as new SKUs, packaging, and edge cases emerge.