mashgin - AI Data Labeler
Requirements
• This role will last for 4-8 weeks, is a part time contractor role, time-off during holidays, and has flexible hours! The pay is $15 per hour. • 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. • Prior experience labeling data for computer vision, robotics, autonomous vehicles, or medical imaging. • Working knowledge of ML evaluation concepts such as precision, recall, IoU, and confusion matrices. • Experience with hardware troubleshooting, QA processes, or lab environments. • Background in roles requiring meticulous inspection (e.g., QA, lab work, manufacturing inspection).
Responsibilities
• 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.
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