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Strong brand, entry-level generalist title, and common annotation role increase applicant competition.
Annotation and basic data skills are easily transferable across industries.
No explicit years or certifications; focus is on attention to detail and basic skills.
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Label and validate diverse datasets for document fraud, deepfake, PAD, and face recognition to enhance model accuracy and fairness.
Perform detailed annotations such as identifying document elements, marking manipulated regions, and applying time-based video labels.
Review peer annotations, document edge cases, and collaborate with ML engineers to improve data quality and annotation guidelines.
Attention to detail with ability to maintain accuracy on repetitive tasks.
Basic understanding of computer vision/ML concepts (training provided).
Comfortable using web-based annotation tools (training provided).
Work Experience Required: Not explicitly mentioned in the JD.
Operates well in detail-oriented, repetitive data annotation tasks that support machine learning pipelines.
Able to communicate clearly about complex or edge-case data observations.
Fits a role in collaborative environments interfacing between annotation work and ML engineering.