David Isaac

Key Skills

Software

Appen

Data Annotation Tech

Top Subject Matter

No subject matter listed

Top Data Types

Text

Top Label Types

Entity Ner Classification

Classification

RLHF

Evaluation Rating

Freelancer Overview

I am an experienced AI trainer and data annotation specialist with a strong background in refining large language models (LLMs) to improve their accuracy and conversational abilities. My work spans platforms like Appen and DataAnnotation.tech, where I have spent thousands of hours on RLHF projects, ranking model responses, fact-checking data, and rewriting AI-generated text to sound more natural. I excel at identifying model hallucinations and subtle logic errors and ensuring that annotated data meets rigorous quality standards. My expertise also includes content moderation, prompt engineering, and intent classification, with a particular focus on technical domains such as sports analytics. I am dedicated to delivering high-quality training data that enables AI systems to perform reliably and safely in real-world applications.

Labeling Experience

AI Content Specialist & Evaluator

Data Annotation TechTextClassificationRLHF

My work here focuses on high-level AI model training and evaluation using text-based datasets. The scope involves Reinforcement Learning from Human Feedback (RLHF), where I perform Side-by-Side (SxS) comparisons to rank model responses based on helpfulness, tone, and factual integrity. My specific tasks include identifying and correcting model hallucinations, prompt engineering, and rewriting AI outputs to ensure a natural, human-like linguistic flow. I handle a consistent volume of complex tasks weekly, adhering to strict quality measures by passing frequent 'Gold Set' hidden tests and maintaining high-precision alignment with the project's core guidelines.

My work here focuses on high-level AI model training and evaluation using text-based datasets. The scope involves Reinforcement Learning from Human Feedback (RLHF), where I perform Side-by-Side (SxS) comparisons to rank model responses based on helpfulness, tone, and factual integrity. My specific tasks include identifying and correcting model hallucinations, prompt engineering, and rewriting AI outputs to ensure a natural, human-like linguistic flow. I handle a consistent volume of complex tasks weekly, adhering to strict quality measures by passing frequent 'Gold Set' hidden tests and maintaining high-precision alignment with the project's core guidelines.

2024 - 2025

Data Labeler

AppenTextEntity Ner Classification

At Appen, the project scope was centered on large-scale data labeling and search relevance evaluation. The data types were primarily search queries and short-form text. My specific tasks included Intent Classification (identifying user goals), Named Entity Recognition (NER), and content moderation to ensure safety and policy compliance. This was a high-volume project where I processed hundreds of tasks per week. To ensure data quality, I followed detailed Standard Operating Procedures (SOPs) and consistently achieved high accuracy scores on regular quality audits and cross-verification checks.

At Appen, the project scope was centered on large-scale data labeling and search relevance evaluation. The data types were primarily search queries and short-form text. My specific tasks included Intent Classification (identifying user goals), Named Entity Recognition (NER), and content moderation to ensure safety and policy compliance. This was a high-volume project where I processed hundreds of tasks per week. To ensure data quality, I followed detailed Standard Operating Procedures (SOPs) and consistently achieved high accuracy scores on regular quality audits and cross-verification checks.

2023 - 2024

Education

Work History