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AI Summary
About Mashgin
Mashgin powers the world's best checkout experience for over 40 million users. Customers just place their items on our kiosks and our AI rings up their entire order in less than a second. With Mashgin, lines are now optional.
Mashgin's technology powers over 1 billion transactions at your favorite locations, including over half of all US professional sports teams, 4,000 convenience stores, major airports, universities, and more.
We’re not just building cutting-edge AI—we’re creating real-world impact and unforgettable experiences. Backed by a well-funded Series B, we’re also one of the rare AI startups that’s already profitable.
Our secret? A culture of extreme ownership, autonomy, and customer obsession. At Mashgin, we are building something extraordinary by challenging conventional wisdom. We’ve thrown out the old rules to focus on what truly matters: creating a kickass product that makes people say, 'Wow'. We don’t care about short-term wins; we build systems that stand the test of time. If you thrive in a culture of excellence without compromise and want to see your work have an immediate, remarkable impact, you’re in the right place.
Position Summary
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.
\nData 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.
• 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).
Mashgin is proud to be an equal opportunity employer. Individuals seeking employment at Mashgin are considered without regards to race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, gender identity, or sexual orientation.
Notice on fraudulent jobs
We have been made aware of instances of fraudulent job postings and/or fraudulent recruiting activity by bad actors, claiming to represent Mashgin. These fraudulent schemes often seek monetary contributions or payments from job seekers (such as for "start up costs" or "equipment"), or seek to collect sensitive personal or banking information from job seekers. These job postings and offers are not authorized by Mashgin, and Mashgin is not responsible for fraudulent offers or requests for personal information or payments. Mashgin will never ask for any financial commitment or contribution from a candidate at any stage of the recruitment process. Candidates who have questions about the validity of Mashgin job postings or offers should consult the job postings on our mashgin.com career site. If you think you've been scammed, please reference this site for more information.
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