Scale's core business is data labeling at massive scale: millions of labeled images for autonomous driving (bounding boxes, segmentation masks, lane markings), text annotations for NLP (named entities, sentiment, intent classification), and RLHF preference data for LLM alignment. They manage a global workforce of labelers with specialized quality control processes — labeling for AI requires consistency that crowdsourcing platforms alone can't provide.
Scale's RLHF services illustrate the human infrastructure behind AI alignment. Skilled annotators compare model outputs, rate responses for helpfulness and harmlessness, and provide the preference data that drives DPO/RLHF training. The quality of these annotations directly affects model behavior — inconsistent or biased labeling produces inconsistently aligned models. Scale invests heavily in annotator training, guidelines, and inter-annotator agreement metrics.