Sierra

Scale is a San Francisco-based data infrastructure company founded in 2016 that builds full-stack technologies for training, evaluating, and deploying machine learning systems at production scale. The platform addresses the core bottleneck in modern AI development: generating high-quality training data and managing the complete ML lifecycle from data generation through model evaluation, safety alignment, and deployment oversight. Scale has processed over 15 billion human decisions to train AI models and paid out $1 billion to contributors globally, operating a distributed workforce model that combines human judgment with systematic data infrastructure. The company's technical focus centers on reinforcement learning from human feedback (RLHF), data generation pipelines, and evaluation frameworks for large language models and generative systems. Their platform provides tools for model evaluation and safety alignment - critical infrastructure for organizations deploying AI in high-stakes environments where reliability and failure mode analysis matter. Scale's customer base spans AI research labs developing frontier models, government agencies with mission-critical requirements, and Fortune 500 enterprises managing production ML systems. This positioning reflects the operational reality that advanced models require both cutting-edge training infrastructure and rigorous evaluation processes to meet deployment standards in regulated or safety-sensitive contexts. With a team of 1,000 people, Scale has evolved from an annotation platform into a full-stack AI infrastructure provider. The company's architecture handles the operational complexity of coordinating human feedback at scale while maintaining the throughput and quality requirements of modern generative model training. Their emphasis on evaluation, safety, and oversight infrastructure addresses the gap between model capabilities and production readiness - the latency between research breakthroughs and deployable systems that enterprises and government agencies can trust for critical decisions.