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Scale AI
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TextI am an experienced AI data annotator with a strong background in labeling and evaluating text, audio, and model outputs in multiple languages, including English, Spanish, and Italian. My work involves applying detailed rubrics to complex and ambiguous data, assessing model reasoning, and ensuring high-quality, reliable datasets for natural language processing tasks. I have a keen eye for patterns and inconsistencies, which allows me to refine annotation strategies and improve overall data clarity. My experience also includes interpreting evolving guidelines, maintaining accuracy under shifting criteria, and supporting process improvement initiatives, making me adaptable and detail-oriented in fast-paced environments. With a foundation in data analysis and research, I excel at synthesizing information and contributing to high-impact AI training data projects.
The Apollo project entails reviewing and modifying existing rubric items for LLM responses, grading them on atomicity, prompt specificity, verifiability, and importance.
The Aether project involves a variety of data labeling tasks designed to train and refine LLMs. Audio tasks entailed reviewing transcription quality, annotating audio segments to mark clarity, subject matter, and tone, and annotating existing transcriptions with detailed prosodic features.
Bachelor of Arts, Global Studies
Manager of Global Programs
Program Intern
