<div>About <a href="https://himalayas.app/companies/mashgin">Mashgin</a></div><div><a href="https://himalayas.app/companies/mashgin">Mashgin</a> 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 <a href="https://himalayas.app/companies/mashgin">Mashgin</a>, lines are now optional.</div><div><a href="https://himalayas.app/companies/mashgin">Mashgin</a>'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.</div><div>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.</div><div>Our secret? A culture of extreme ownership, autonomy, and customer obsession. At <a href="https://himalayas.app/companies/mashgin">Mashgin</a>, 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.</div><div>Position Summary</div><div><p>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.</p></div><h3>You Will Be</h3><ul><div><h3>Data Annotation</h3><p>• Label images and video frames with bounding boxes, polygons, segmentation masks, and SKU-level classifications.</p><p>• Annotate edge cases, including occlusion, overlapping items, glare, motion blur, and unusual product orientations.</p><p>• Maintain consistency against the labeling taxonomy and follow detailed annotation guidelines.</p><p>• Tag training, validation, and test data to support model development and evaluation.</p><h3>Quality Assurance</h3><p>• Compare model predictions to ground-truth labels and document failure modes.</p><p>• Audit annotations from peers and contractors to enforce inter-annotator agreement.</p><p>• Flag systemic issues such as recurring misclassifications, mislabeled SKUs, or low-quality captures.</p><p>• Review confusion matrices and error reports with the ML team to prioritize fixes.</p><h3>Hardware &amp; Software Validation</h3><p>• Identify capture issues that indicate hardware problems: blurry frames, poor