Scale is a San Francisco-based data infrastructure company founded in 2016 that builds full-stack AI platforms for machine learning model development and deployment. The company operates data annotation and machine learning lifecycle management systems that support AI development across enterprises, government agencies, and AI research labs. With a team of 1,000 people, Scale has processed over 15 billion human decisions to train AI models and paid out $1 billion globally to contributors in its data generation network.
The company's core technical infrastructure addresses the machine learning lifecycle through two primary platform capabilities: a data infrastructure system handling Reinforcement Learning from Human Feedback (RLHF), data generation, model evaluation, and safety alignment; and operational tooling for building, deploying, and overseeing AI applications in production. These systems are designed to support large language models (LLMs) and generative models, with particular emphasis on reliability requirements for critical decision-making contexts. The platform architecture combines automated data processing pipelines with human-in-the-loop evaluation mechanisms.
Scale's technical domains span data infrastructure for AI, machine learning lifecycle management, model evaluation frameworks, and safety alignment systems. The company serves customers including Fortune 500 companies, government agencies, and AI labs, providing infrastructure that addresses constraints around data quality, model performance validation, and operational oversight. The platform's design reflects requirements for systems that must maintain reliability standards in production deployments where AI decisions have significant operational or safety implications.
