Kei Okamoto

Key Skills

Software

Scale AI

Data Annotation Tech

Labelbox

Top Subject Matter

No subject matter listed

Top Data Types

Text

Audio

Top Task Types

RLHF

Fine Tuning

Evaluation Rating

Audio Recording

Transcription

Freelancer Overview

I have hands-on experience in AI data evaluation, data annotation, and transcription, with a strong track record of improving data quality and process efficiency. My recent roles include reviewing AI-generated Japanese responses, developing quality guidelines, and conducting detailed audio transcription using platforms like Labelbox. I am skilled in data collection, cleaning, and classification, having managed large datasets for academic research and business operations. My technical toolkit includes Python for automation and web scraping, as well as Excel VBA for workflow optimization. I thrive in multicultural environments, communicate effectively across language barriers, and consistently seek logical, practical solutions to enhance data accuracy and operational efficiency.

Labeling Experience

Japanese AI Response Evaluation & Guideline Development

Data Annotation TechTextRLHFFine Tuning

(The same above) In this project, I worked on evaluating AI-generated responses in Japanese, ensuring linguistic accuracy, consistency, and alignment with predefined quality standards. My tasks included rating responses based on RLHF (Reinforcement Learning from Human Feedback) criteria, refining evaluation guidelines, and developing selection standards for assessing AI-generated text. Additionally, I reviewed and improved criteria created by other evaluators to enhance the reliability of the assessment process. This role required a deep understanding of Japanese language nuances, critical thinking, and the ability to systematically analyze AI outputs to provide high-quality training data for LLM (Large Language Model) development.

(The same above) In this project, I worked on evaluating AI-generated responses in Japanese, ensuring linguistic accuracy, consistency, and alignment with predefined quality standards. My tasks included rating responses based on RLHF (Reinforcement Learning from Human Feedback) criteria, refining evaluation guidelines, and developing selection standards for assessing AI-generated text. Additionally, I reviewed and improved criteria created by other evaluators to enhance the reliability of the assessment process. This role required a deep understanding of Japanese language nuances, critical thinking, and the ability to systematically analyze AI outputs to provide high-quality training data for LLM (Large Language Model) development.

2024

Japanese AI Response Evaluation & Guideline Development

Scale AITextRLHFFine Tuning

In this project, I worked on evaluating AI-generated responses in Japanese, ensuring linguistic accuracy, consistency, and alignment with predefined quality standards. My tasks included rating responses based on RLHF (Reinforcement Learning from Human Feedback) criteria, refining evaluation guidelines, and developing selection standards for assessing AI-generated text. Additionally, I reviewed and improved criteria created by other evaluators to enhance the reliability of the assessment process. This role required a deep understanding of Japanese language nuances, critical thinking, and the ability to systematically analyze AI outputs to provide high-quality training data for LLM (Large Language Model) development.

In this project, I worked on evaluating AI-generated responses in Japanese, ensuring linguistic accuracy, consistency, and alignment with predefined quality standards. My tasks included rating responses based on RLHF (Reinforcement Learning from Human Feedback) criteria, refining evaluation guidelines, and developing selection standards for assessing AI-generated text. Additionally, I reviewed and improved criteria created by other evaluators to enhance the reliability of the assessment process. This role required a deep understanding of Japanese language nuances, critical thinking, and the ability to systematically analyze AI outputs to provide high-quality training data for LLM (Large Language Model) development.

2024

Artificial Intelligence, NLP, Speech Recognition

LabelboxAudioTranscription

Worked on a short-term transcription and annotation project for Alignerr, focusing on high-quality speech data preparation for AI training. Transcribed 10–30 second audio clips using the Labelbox platform, applying detailed guidelines for fillers, speaker overlaps, and unclear segments. Ensured accuracy and consistency through careful listening, tagging, and adherence to strict quality standards.

Worked on a short-term transcription and annotation project for Alignerr, focusing on high-quality speech data preparation for AI training. Transcribed 10–30 second audio clips using the Labelbox platform, applying detailed guidelines for fillers, speaker overlaps, and unclear segments. Ensured accuracy and consistency through careful listening, tagging, and adherence to strict quality standards.

2025 - 2025

Education

Work History