Data Annotation Analyst

Atoms•San Francisco, CA

5d•$71,000 - $89,000•Onsite

About The Position

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.

Requirements

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

Responsibilities

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