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Text GenerationOver the past several years, I have built hands-on experience contributing to AI training data pipelines through transcription, annotation, and model output evaluation. In my contract role with DataAnnotation Tech, I annotated text and audio datasets used directly in large language model training and benchmarking, developed evaluation rubrics for American English audio tasks, and graded model outputs against predefined criteria for fluency, accuracy, and instructional clarity. My linguistics background — including graduate-level research on American English dialect variation at the University of Washington — gives me an analytical edge most transcriptionists lack: I don't just capture what was said, I understand why certain phonological, register, or dialectal features matter for model performance. What sets me apart is the combination of native fluency, academic training in applied linguistics, and practical QA experience across the full annotation workflow — from task design and rubric development through consistency review before dataset integration. I am comfortable identifying edge cases that generalist annotators miss: overlapping speech, tonal ambiguity, regionally marked vocabulary, and shifts between formal and informal American English registers. My research assistant work at the University of Washington grounded me in rigorous documentation and structured data handling, which translates directly into the consistency and reliability that AI benchmarking projects demand.
TextTranscriptionI annotated text and audio datasets used directly in large language model training and benchmarking, developed evaluation rubrics for American English audio tasks, and graded model outputs against predefined criteria for fluency, accuracy, and instructional clarity.
Master's Degree, Applied Linguistics
Bachelor's, Linguistics and communication
Freelance Audio Transcriptionist & Content Evaluator
Content & Communications Associate
