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

Information Technology

11-50 (30)

Founded 2014

$74M raised

6 open positions

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AI Data Labeler

1 month, 3 weeks ago

Anywhere

Contract

Junior

Data Annotator

Artificial Intelligence and Machine Learning

Computer Vision

Apply Now

Mashgin

Technology Hardware, Storage & Peripherals

11-50

Founded 2014

$74M raised

View All Jobs 6

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

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