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Multimodal AI data evaluation and annotation. Brings 6+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal, Proprietary Tooling, and Labelbox. Education includes Master's, Colorado State University (2020). AI-training focus includes data types such as Audio, Image, and Video and labeling workflows including Evaluation, Rating, and Object Detection.
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Reviewed, assessed, and annotated multimedia artifacts for large-scale AI training efforts, including over 10,000 audio files, 50,000 images, and 5,000 video clips. Performed side-by-side comparative analysis of AI-generated outputs, including images produced by Stable Diffusion and audio produced by GPT, evaluating realism, logical coherence, and overall visual/audio appeal. Tagged and annotated objects, scenes, actions, and auditory events in video and audio files. Followed detailed project guidelines, achieving a 98%+ quality assurance rate while maintaining fidelity for a team of 10 annotators. Participated in a project that improved multimodal AI model reliability by 15% through reduced output errors.
Quality assurance and consistency review of annotations against project guidelines.
Annotation of objects, scenes, actions, and auditory events in multimedia files.
Side-by-side comparative analysis of AI-generated images and audio, evaluating realism, logical coherence, and overall visual/audio appeal.
Master's, Computer Science
Digital Annotation Specialist