Annotation is often described as a low-skill task. We have found the opposite to be true. The annotators who produce the highest-quality labels across our most complex projects — medical imaging, legal NLP, autonomous driving — share a cluster of skills that are genuinely difficult to find and develop: domain knowledge, tolerance for ambiguity, and a disciplined consistency that does not degrade across repetitive tasks. Hiring for these traits, rather than for speed, is the single biggest factor that separates our quality tier from commodity labeling.
Domain knowledge matters more than most organisations expect. An annotator with clinical training will consistently produce higher-quality medical image labels than a generalist, even after the generalist receives detailed guidelines. This is not because the guidelines are inadequate — it is because domain experts bring an internalized sense of what matters at the boundary cases, the exact situations where guidelines run out and judgment takes over. For our specialist datasets, we source annotators from relevant professional backgrounds and treat the annotation work as a skills-adjacent activity rather than a general labour task.
We are actively expanding our annotation team across NLP, computer vision, and time series domains. We are particularly interested in candidates with backgrounds in medicine, law, finance, and geospatial analysis. If you are detail-oriented, intellectually curious, and interested in the infrastructure of AI, we would love to hear from you. Visit our Careers page or reach out directly — we review every application personally.