AI trainer
Also known as: RLHF labeler, human feedback specialist, AI data labeler, AI evaluator
RLHF, reinforcement learning from human feedback, is one of the core techniques that made models like ChatGPT feel helpful rather than just statistically plausible. It works by having humans rate or compare model outputs, which then shapes how the model is trained. AI trainers are the people doing that feedback work. The role ranges from contractors doing high-volume annotation to domain experts providing nuanced signal on specialized topics.
Microsoft's research into emerging AI roles identified AI trainer as one of the titles growing in importance as dedicated prompt engineer roles declined. The core skill is less about prompting and more about understanding what good model behavior looks like and articulating that consistently. In legal or medical contexts, for example, a qualified professional might serve as a trainer to ensure model outputs meet domain standards.
For most builders, AI trainers are an invisible layer in the models they use. But teams building custom fine-tuned models or running systematic evals need to think carefully about who is providing the feedback signal, what biases they might introduce, and how to structure the process. As RLHF and its variants become more accessible, smaller organizations are running their own training pipelines and need people who understand how to provide quality signal.