New York

I’m an AI Data Annotator and Linguistics graduate, focused on delivering accurate, consistent annotations that improve AI model performance and reliability. In a remote, Australia-based research environment, I complete onboarding and training requirements while strictly following project guidelines and NDAs.

I’m detail-oriented and quality-driven, using structured workflows to meet predefined quality metrics and turnaround times across assigned tasks. I identify, analyze, and escalate anomalies, trends, and recurring issues to the Project Manager, helping reduce systemic errors and strengthen annotation consistency. I stay proactively aligned on progress, shifting priorities, and potential risks while troubleshooting macOS-based tooling to minimize disruption.

Before my current annotation work, I supported computational linguistics research as a Research Assistant in Sydney, helping transcribe, code, and label spoken-language corpora. I applied phonological and syntactic annotation frameworks to large datasets, contributed to inter-annotator agreement through documentation of inconsistencies, and helped improve team-wide guideline standards through revisions. I completed a Bachelor of Arts in Linguistics (with Distinction), including an honours thesis on an annotation consistency quality assurance framework for neural machine translation corpora.