AI Data Labeler

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.

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.

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.

•  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.