Joseph Mburu

Key Skills

Software

Appen

Top Subject Matter

No subject matter listed

Top Data Types

Text

Top Label Types

Entity Ner Classification

Classification

Text Generation

RLHF

Prompt Response Writing SFT

Freelancer Overview

I am a detail-oriented and analytical remote freelancer with hands-on experience in data labeling, AI data annotation, and training data evaluation for NLP projects. My work has included structured data labeling, text classification, sentiment analysis, search relevance evaluation, and prompt/LLM response rating on platforms like Microworkers, OneForma, and Clickworker. I am highly skilled at interpreting complex guidelines, applying logical frameworks, and ensuring high-quality, consistent outputs under tight deadlines. I have evaluated AI-generated content for coherence, factual accuracy, and policy compliance, and am comfortable working independently on large datasets. My background in legal research and structured reasoning further strengthens my ability to deliver precise, reliable results for AI training and data annotation tasks.

Labeling Experience

AI Response Evaluation & NLP Text Annotation for LLM Training

AppenTextEntity Ner ClassificationClassification

Worked as a Remote AI Data Annotator and Evaluator contributing to the training and alignment of Large Language Models (LLMs). The project involved evaluating, labeling, and improving AI-generated text to enhance model accuracy, safety, and contextual understanding. Scope of Work: Evaluated AI-generated responses for factual accuracy, logical consistency, coherence, neutrality, and completeness. Applied structured evaluation rubrics to rate responses across multiple quality dimensions. Identified hallucinations, misleading content, unsafe outputs, and policy violations. Performed Reinforcement Learning from Human Feedback (RLHF) tasks by ranking multiple model outputs based on quality and alignment. Conducted sentiment analysis, intent classification, and topic categorization. Performed Named Entity Recognition (NER) tagging on legal, general knowledge, and conversational datasets. Assisted in supervised fine-tuning (SFT) by writing high-quality prompt-response pairs.

Worked as a Remote AI Data Annotator and Evaluator contributing to the training and alignment of Large Language Models (LLMs). The project involved evaluating, labeling, and improving AI-generated text to enhance model accuracy, safety, and contextual understanding. Scope of Work: Evaluated AI-generated responses for factual accuracy, logical consistency, coherence, neutrality, and completeness. Applied structured evaluation rubrics to rate responses across multiple quality dimensions. Identified hallucinations, misleading content, unsafe outputs, and policy violations. Performed Reinforcement Learning from Human Feedback (RLHF) tasks by ranking multiple model outputs based on quality and alignment. Conducted sentiment analysis, intent classification, and topic categorization. Performed Named Entity Recognition (NER) tagging on legal, general knowledge, and conversational datasets. Assisted in supervised fine-tuning (SFT) by writing high-quality prompt-response pairs.

2023

Education

Work History