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AI Data Labeler

1 month, 1 week ago
Anywhere
Contract
Junior
Data Annotator
Artificial Intelligence and Machine Learning
Computer Vision
Apply Now
Mashgin

Mashgin

Mashgin is a leading provider of AI-powered self-checkout systems that revolutionize retail experiences. Their touchless system, utilizing 3D reconstruction and deep learning, eliminates barcodes and wait times, offering the fastest and easiest checkou...

Technology Hardware, Storage & Peripherals
11-50
Founded 2014
$74M raised
View All Jobs 3

Description

  • Label images and video frames using bounding boxes, polygons, segmentation masks, and SKU-level classifications.
  • Annotate difficult visual cases such as occlusion, overlapping items, glare, motion blur, and unusual product orientations.
  • Tag training, validation, and test data to support model development and evaluation.
  • Compare model predictions to ground-truth labels and document failure modes.
  • Audit peer and contractor annotations to maintain inter-annotator agreement and labeling consistency.
  • Flag recurring issues such as misclassifications, mislabeled SKUs, and poor-quality captures.
  • Review confusion matrices and error reports with the ML team to prioritize fixes.
  • Identify hardware-related capture problems such as blurry frames, poor lighting, color shifts, dropped frames, and camera misalignment.
  • Test devices in lab and field conditions to verify image quality and checkout accuracy.
  • Reproduce and document software bugs found through labeling workflows or production telemetry.
  • Maintain and refine labeling guidelines as new SKUs, packaging, and edge cases emerge.
  • Write concise reports on labeling trends, error patterns, and recommendations.
  • Collaborate with ML engineers, hardware engineers, product, and operations teams.

Requirements

  • 2+ years of experience labeling data for computer vision, robotics, autonomous vehicles, or medical imaging.
  • Exceptional attention to detail and comfort with repetitive, precision-oriented work.
  • Experience with annotation tools such as CVAT, Labelbox, SuperAnnotate, Scale, or in-house tooling.
  • Ability to follow detailed written guidelines and document ambiguous cases instead of guessing.
  • Strong written communication skills for structured QA reports and Slack updates.
  • Comfort working with images and video from physical devices and reasoning about visual edge cases.
  • Working knowledge of ML evaluation concepts such as precision, recall, IoU, and confusion matrices (preferred).
  • Experience with hardware troubleshooting, QA processes, or lab environments (preferred).
  • Background in meticulous inspection roles such as QA, lab work, or manufacturing inspection (preferred).

Interested in this position?

Apply directly on the company website

Apply Now

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