Zoey Lee

Key Skills

Software

Label Studio

Internal/Proprietary Tooling

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Computer Code Programming

Text

Top Task Types

Audio Recording

Classification

Computer Programming Coding

Translation Localization

Freelancer Overview

I have hands-on experience in NLP projects involving data labeling and text classification for AI training. My work includes annotating and categorizing large-scale text datasets, analyzing label quality, and designing labeling criteria to ensure consistency and usefulness for model training. Through these projects, I developed a strong understanding of how high-quality training data directly impacts LLM performance and evaluation outcomes. With a Computer Science background, I am also comfortable working with programming and technical content. I have experience using code to preprocess data, analyze results, and support AI-related workflows. In addition, I am fluent in both Korean and English, allowing me to contribute effectively to bilingual LLM evaluation, prompt analysis, and language-specific data labeling tasks.

Labeling Experience

AI Developer task designer

Internal Proprietary ToolingComputer Code ProgrammingComputer Programming Coding

Contributed to a data creation project aimed at improving frontier LLMs in solving realistic, developer-style tasks within terminal-based environments. The project focused on enabling AI systems to reason, plan, and execute solutions similarly to real software engineers. My role involved designing realistic, work-based problem scenarios that reflect actual developer workflows, defining clear task requirements, and validating task difficulty and feasibility. I participated in quality reviews to ensure tasks accurately tested instruction-following, technical reasoning, and tool usage in command-line environments. This work directly supported the creation of high-quality training and evaluation data for advanced AI agent development.

Contributed to a data creation project aimed at improving frontier LLMs in solving realistic, developer-style tasks within terminal-based environments. The project focused on enabling AI systems to reason, plan, and execute solutions similarly to real software engineers. My role involved designing realistic, work-based problem scenarios that reflect actual developer workflows, defining clear task requirements, and validating task difficulty and feasibility. I participated in quality reviews to ensure tasks accurately tested instruction-following, technical reasoning, and tool usage in command-line environments. This work directly supported the creation of high-quality training and evaluation data for advanced AI agent development.

2025

Business Analyst

Label StudioAudioEvaluation Rating

Performed data labeling and transcription tasks to support AI and LLM training pipelines. The work involved accurately transcribing and annotating text data while strictly following detailed guidelines to ensure consistency, clarity, and high data quality. I reviewed and corrected outputs to maintain alignment with annotation standards, contributing to reliable training datasets used for model learning and evaluation. This experience strengthened my attention to detail, language accuracy, and understanding of how high-quality labeled data impacts overall AI model performance.

Performed data labeling and transcription tasks to support AI and LLM training pipelines. The work involved accurately transcribing and annotating text data while strictly following detailed guidelines to ensure consistency, clarity, and high data quality. I reviewed and corrected outputs to maintain alignment with annotation standards, contributing to reliable training datasets used for model learning and evaluation. This experience strengthened my attention to detail, language accuracy, and understanding of how high-quality labeled data impacts overall AI model performance.

2025 - 2025

Education

Work History