We are hiring an AI Data Labeler / Computer Vision Annotator to serve as the ground truth backbone of our computer vision pipeline. In this role, you will annotate the images and video our hardware captures, audit model predictions against reality, and surface patterns that tell us when our cameras, lighting, or models are not performing as expected. Your work directly determines how accurately our system identifies products in the real world.
You Will Be
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.
Minimum Qualifications
• 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.
Preferred Qualifications
• 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).
Originally posted on Himalayas