Every AI model you've heard of — chat assistants, image generators, self-driving systems — was trained on data prepared by people. Behind every impressive model release is a workforce of human contributors who labeled the examples, judged the outputs, and created the data that taught the model what "good" looks like. When you work on HumanSignal Labs projects, you're part of that workforce.
Your judgment teaches the model
AI models don't learn values, accuracy, or nuance on their own. They learn from human feedback:
- When you rate or rank model responses, you're teaching the model which answers are helpful, honest, and safe.
- When you annotate images, audio, or text, you're building the ground truth that models are trained and measured against.
- When you red team a model, you're finding failures before real users do — making the model safer for everyone.
The quality of your work directly shapes the quality of the models. A carefully labeled dataset produces a model that behaves reliably; a sloppy one produces a model that fails in unpredictable ways. This is why we invest so much in training, guidelines, and quality review — and why careful contributors are the most valuable people in our network.
Data no one else can create
HumanSignal Labs specializes in the data problems that can't be solved by scraping the internet: new data created from scratch, expert judgments in specialized fields, and multimodal data like studio-grade audio, photo, and video. Frontier AI labs come to us precisely because this data doesn't exist anywhere else — until contributors like you create it.
Real experts, treated fairly
We believe the people training AI should be treated as skilled professionals. That means fair pay, transparent rates disclosed before you start, documented consent for data collection work, and support from real humans when you need it. Ethical sourcing isn't a marketing line for us — it's a requirement our clients depend on.