AI Data Labeler

Data Annotation

• 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.

• Tag training, validation, and test data to support model development and evaluation.

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.

• Review confusion matrices and error reports with the ML team to prioritize fixes.

Hardware & Software Validation

• Identify capture issues that indicate hardware problems: blurry frames, poor lighting, color shifts, dropped frames, or camera misalignment.

• Test devices in lab and field conditions to confirm image quality and end-to-end checkout accuracy.

• Reproduce and document software bugs surfaced by labeling workflows or production telemetry.

• Partner with hardware and software engineers to validate fixes and run regression checks.

Process & Communication

• Maintain and refine internal labeling guidelines as new SKUs, packaging, and edge cases emerge.

• Write concise reports summarizing labeling trends, error patterns, and recommendations.

• Collaborate cross-functionally with ML engineers, hardware engineers, product, and operations.

•  2+ years of experience labeling data for computer vision, robotics, autonomous vehicles, or medical imaging.

• Exceptional attention to detail and high tolerance for repetitive, precision-oriented work.

• Experience with annotation tools such as CVAT, Labelbox, SuperAnnotate, Scale, or in-house tooling.

• Comfort following detailed written guidelines and documenting ambiguous cases instead of guessing.

• Strong written communication for clear, structured QA reports and Slack updates.

• Comfort working with images and video from physical devices, and reasoning about visual edge cases.