Zefr is the leader in AI-powered content classifications for brands and advertisers. Backed by Felicis and IVP.
About the role
Every AI system that can tell a cooking video from a combat video learned it from a person who made that call first. That person is you. We're hiring a Multilingual Data Annotation Specialist to label video, image, and metadata content across YouTube, TikTok, Meta, and Snap, and to bring cultural fluency to calls that a model can't make on its own. A clip that reads as harmless in one market reads very differently in another. Catching that difference is the whole job. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Strong cultural fluency and a feel for context across varied content types
- Regular user of TikTok, YouTube, Facebook, Instagram, and Snapchat. You should know these platforms as a native, not a visitor
- Exceptional attention to detail, sustained across repetitive work
- Clear written communication, especially when explaining ambiguity, inconsistency, or definitional drift
- Comfort applying style guides and holding consistency across formats
- Comfortable using AI tools such as Claude, Gemini, or ChatGPT
More about this role
Zefr is the leading global technology company enabling responsible marketing in walled garden social environments. Our solutions let brands manage where their advertising shows up on YouTube, Meta, TikTok, and Snap, in line with industry-standard frameworks. Through patented AI technology, Zefr gives brands and agencies more accurate and transparent tools for social walled gardens. We're headquartered in Los Angeles, with additional locations across the globe.
Every AI system that can tell a cooking video from a combat video learned it from a person who made that call first. That person is you.
We're hiring a Multilingual Data Annotation Specialist to label video, image, and metadata content across YouTube, TikTok, Meta, and Snap, and to bring cultural fluency to calls that a model can't make on its own. A clip that reads as harmless in one market reads very differently in another. Catching that difference is the whole job.
Your labels become the ground truth our machine learning systems train on and get measured against. When you get a hard call right, the system gets better at it. When the guidelines don't cover the case in front of you, you're the one who tells us the...
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