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TextI have hands-on experience contributing to AI training data across text annotation, supervised fine-tuning (SFT), and red teaming. My work includes designing prompts that meet strict criteria, writing high-quality reference responses, refining pre-generated rubric items, and systematically evaluating model outputs for accuracy, safety, and guideline adherence. I have also performed red teaming by creating adversarial and edge-case scenarios to identify failure modes, unsafe behaviors, and boundary violations, documenting findings to support model improvement and risk mitigation. What distinguishes me is a strong domain-informed, detail-oriented approach shaped by advanced scientific training. I am skilled at interpreting complex guidelines, resolving ambiguous cases, and maintaining consistency across large annotation efforts.
Creating prompts that fits a specific criteria, writing a sample response and evaluating the response of current models.
Using domain knowledge to fine-tune pre-generated rubric items.
Using domain knowledge for red teaming an existing LLM.
Doctor of Philosophy, Horticulture and Agronomy
Bachelor of Science, Chemistry
Graduate Researcher
