Kasey Worsey

Key Skills

Software

CrowdSource

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Freelancer Overview

I am an experienced AI trainer and data annotation specialist with over four years of hands-on work across leading platforms such as OpenAI, Scale AI, Remotasks, Appen, and DataAnnotation.tech. My expertise spans large language model (LLM) training, reinforcement learning from human feedback (RLHF), prompt engineering, and high-precision data labeling for both NLP and computer vision projects. I have completed more than 10,000 annotation tasks with a 98%+ accuracy rate, contributing to model improvements in conversational AI, sentiment analysis, content moderation, image recognition, and specialized domains like medical, legal, and financial AI. I am skilled with tools like Label Studio, CVAT, Doccano, and Prodigy, and have a proven track record in quality assurance, edge case identification, and detailed feedback. My approach centers on accuracy, adaptability, and ethical AI development, with a passion for advancing AI safety, alignment, and human-AI interaction through rigorous training data and evaluation.

Labeling Experience

Pharmaceutical Representative

CrowdsourceTextText Summarization

The project focused on large-scale data annotation to support the training and optimization of machine learning models. The scope included labeling structured and unstructured datasets such as text, images, and tabular data to improve model accuracy in classification, entity recognition, and sentiment analysis tasks. The objective was to ensure high-quality, consistent, and contextually accurate annotations that aligned with predefined client guidelines and AI model requirements. Specific data labeling tasks included text categorization, named entity recognition (NER), sentiment tagging, image classification, and bounding box annotation where applicable. I followed detailed annotation guidelines, applied taxonomy standards consistently, and flagged ambiguous cases for review. When necessary, I collaborated with quality analysts to clarify edge cases and maintain labeling consistency across datasets. The project size involved thousands of data samples processed weekly within structure

The project focused on large-scale data annotation to support the training and optimization of machine learning models. The scope included labeling structured and unstructured datasets such as text, images, and tabular data to improve model accuracy in classification, entity recognition, and sentiment analysis tasks. The objective was to ensure high-quality, consistent, and contextually accurate annotations that aligned with predefined client guidelines and AI model requirements. Specific data labeling tasks included text categorization, named entity recognition (NER), sentiment tagging, image classification, and bounding box annotation where applicable. I followed detailed annotation guidelines, applied taxonomy standards consistently, and flagged ambiguous cases for review. When necessary, I collaborated with quality analysts to clarify edge cases and maintain labeling consistency across datasets. The project size involved thousands of data samples processed weekly within structure

2020 - 2010

Education

Work History