Skill
Data Labeling & Annotation
Data Science, Analytics and AI/ML
Data labeling and annotation is the process of tagging raw data—such as images, text, or audio—with informative labels so that machine learning models can learn from it during supervised training. Annotators and data teams mark objects in images, categorize text sentiment, or transcribe speech to create high-quality training datasets. This work is foundational to computer vision, natural language processing, and other AI applications, and is often performed with specialized tools or crowdsourced labor.
Open roles requiring Data Labeling & Annotation (1)
Forward Deployed Engineer, Audio
Protege
Full-time · Remote
A forward-deployed engineer role supporting Protege's audio data business, combining hands-on technical leadership of customer engagements with platform development. This suits engineers who excel at learning fast in ambiguous settings, owning customer outcomes end-to-end, and building the reusable infrastructure that scales a vertical.
Listed on Protege’s careers site · Apply there ↗
Roles that use Data Labeling & Annotation
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