About the role
Who we are Atoms is building the machines that power the next era of progress. Over the last decade, software has transformed the digital world. But the physical world, where food is made, minerals are mined, goods are moved, and industries are run, remains far less intelligent, far less efficient, and far more constrained. We’re changing that. Atoms builds Physical AI— real-world robots for the industries that move civilization forward, starting with food, mining, and transport. Our systems are designed to understand, predict, and control the real world with precision, turning complex physical operations into something more reliable, more scalable, and more productive. This work requires more than robotics. It requires deep integration across hardware, software, AI, operations, manufacturing, and real estate. We don’t just build machines in a lab. We deploy them into real environments, operate them, learn from them, and improve them until they work at scale. We are roboticists, engineers, operators, and builders. We believe the next great technology companies will not only transform information, but the physical systems that shape everyday life. If you want to work on hard problems with real-world impact, join us. What you’ll do Perform quality control and labeling for images used for machine learning (ML) models Manage a large team of remote labelers Upload prioritized image data for labeling Create and track metrics for labelers Iterate on existing labeling guidelines for multiple types of computer vision tasks Identify common errors in labeling Communicate feedback and priorities to the labeling team Work directly with the ML team What we’re looking for Prior experience labeling or performing quality control for ML training data Detail-oriented approach Excellent communication skills and effective collaboration with members of
the team Familiarity with command-line interface tools preferred Knowledge of labeling policies for multiple types of computer vision tasks Experience with annotation platforms Familiarity with spreadsheet tools or BI dashboards for tracking and visualizing labeler metrics Exposure to version control (Git) or ticketing/workflow tools