Doreen T.

Key Skills

Software

Labelbox

CVAT

Data Annotation Tech

Remotasks

Surge AI

Top Subject Matter

YES

YES

YES

Top Data Types

Text

Image

Top Task Types

Entity (NER) Classification

Classification

Freelancer Overview

I have 1+ years of hands-on experience in data labeling and AI training data preparation for machine learning and LLM projects. My core work involves text annotation, sentiment analysis, intent classification, named entity recognition, and rating chatbot responses for accuracy, helpfulness, and safety as part of RLHF tasks. I’m detail-oriented with a 97-98% accuracy rate and I’m skilled at following complex 40+ page guideline documents without errors. I’ve labeled and reviewed over 10,000+ prompts, Q&A pairs, and images including bounding boxes for object detection datasets. I work efficiently on both mobile and desktop, consistently meeting strict SLAs and deadlines. Key skills I bring include attention to detail, critical thinking, fast typing at 45+ WPM, quality assurance, and the ability to adapt quickly to new project instructions. I’m fluent in English and Swahili, which helps with multilingual datasets and code-switching data. I’m reliable, consistent, and comfortable working remotely for long hours while maintaining high data quality.

Labeling Experience

LLM chatbot Training and Text Annotation Specialist

TextRLHF

Scope: Training data preparation for Large Language Models focused on chatbot interactions and content safety. Tasks performed: 1. Text classification - labeling prompts by intent, topic, and toxicity level 2. RLHF/Ranking - rating 2-4 AI chatbot responses for accuracy, helpfulness, harmlessness, and relevance 3. Named Entity Recognition NER - tagging names, places, dates, organizations in text 4. Sentiment analysis - classifying text as positive, negative, or neutral Project size: Labeled and reviewed 10,000+ text prompts and Q&A pairs between Jan 2024 to June 2026 Quality measures adhered to: Followed 40+ page project guidelines strictly. Maintained 97-98% agreement rate on QA audits. Double-checked all edge cases. Met daily SLA targets with <2% error rate. Worked on mobile + desktop while upholding consistency and accuracy standards.

2024 - Present

Education

Work History