Data Annotator
I have worked on a range of AI training projects on Outlier involving general and medical domains, supporting tasks across multiple data types including text, images, and audio. My responsibilities have included generating and annotating content, evaluating model outputs for accuracy and relevance, and ensuring alignment with detailed project guidelines. In medical-focused tasks, I applied a high level of care to verify information against reliable sources and ensure clarity, safety, and appropriate context in responses. I also contributed to multimodal tasks, such as interpreting image-based prompts and assessing audio-related outputs, ensuring consistency and usability across formats. A key part of my work has been maintaining quality through structured evaluation processes. I consistently applied rubric-based scoring, identified issues such as factual inaccuracies, ambiguity, or flawed reasoning, and revised outputs accordingly. I am experienced working within quality assurance workflows, including moderator reviews and feedback loops, and I use this feedback to continuously improve performance. My approach is detail-oriented and standards-driven, ensuring that all outputs meet established quality benchmarks for accuracy, coherence, and reliability.