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Data Annotation TechNo subject matter listed
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VideoI am an experienced AI software validation specialist with a strong background in data annotation, data quality assurance, and large language model (LLM) validation across diverse domains, including psychology and neuroscience. My work involves designing and testing agentic task flows using JavaScript, conducting rigorous peer reviews of AI-generated outputs, and ensuring ethical standards in model-user interactions. I have hands-on experience managing and organizing multiple data systems, maintaining data privacy, and supporting projects that require high attention to detail and accuracy. My academic background in psychology and neuroscience, combined with my technical and analytical skills, enables me to contribute effectively to projects focused on high-quality AI training data and annotation.
Wrote complex, multi-step reasoning, expert level prompts to create model failure. Afterwards, I created a golden response and/or fine grained rubric criteria depending on the project requirements.
Listened to both human input as well as AI generated audio responses to annotate for errors and artifacts, emotional tone, emphasis, and pace.
General visual-spatial reasoning tasks in which I developed challenging prompts to test the model and then visually demonstrated the train of thought with bounding boxes in external software.
Using a information in a generated universe, I created complex, multistep reasoning requests that an average user might ask Siri or another AI assistant. After this, I created a chain of thought in external software to extract the requested information and output the requests. Functions included booking taxis, creating emails, scheduling events and shopping.
Master of Science, Psychology and Neuroscience of Mental Health
Bachelor of Arts, Psychology
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