Using ML to Improve Labeling Quality on Scale Rapid

Zhichun Li is the Head of Scale Rapid at Scale AI. She built the team from scratch with a focus on providing the fastest way to production-level quality labels within a day, with no data minimums. As an early employee of the company, she built up the infrastructure for our supply ops system and scaled up our 3D Sensor Fusion product. Prior to Scale AI, Zhichun studied CS at CMU and earned her MBA from Yale SOM.

Bryan leads quality efforts at Rapid, ensuring that any use case involving images, videos, text, and more can be supported on Rapid to quickly obtain high quality annotations. Prior to Scale AI, Bryan studied computer science and mathematics at MIT, and was a top competitor in national algorithmic programming contests such as the USACO.

Kiko works on developing and applying machine learning driven systems within Rapid to improve measurement, annotation quality, and overall efficiency. Before joining Scale, he was an ML engineer at Meta (formerly Facebook) focused on personalization and recommendation models for the Facebook Jobs team.