Abdulrahman Hassan

Key Skills

Software

Labelbox

CVAT

Prodigy

Top Subject Matter

Large Language Models

AI Safety

Human Feedback

Top Data Types

Text

3D Sensor

Image

Document

Top Task Types

RLHF

Segmentation

Classification

Freelancer Overview

I specialize in high-precision data annotation and AI training, with a core focus on **Reinforcement Learning from Human Feedback (RLHF)** and dataset curation for Large Language Models (LLMs). My experience is centered on bridging the gap between raw data and machine intelligence by leading complex labeling tasks, including model ranking, "red-teaming" for safety compliance, and high-fidelity computer vision annotation such as **LIDAR and Semantic Segmentation**. I have a proven track record of managing datasets exceeding **500,000 instances** while maintaining a **99.5% accuracy rate** and reducing labeler ambiguity by 35% through the development of rigorous technical guidelines. What sets me apart is the integration of technical automation with a deep understanding of model alignment. I don’t just manually label data; I utilize **Python (Pandas/NumPy)** to automate preprocessing and identify the subtle edge cases that lead to model hallucinations. By focusing on **Inter-Annotator Agreement (IAA)** and recursive quality auditing, I ensure that training data is not only clean but strategically aligned with model performance goals. This combination of analytical rigor and a deep understanding of AI safety protocols allows me to deliver datasets that directly accelerate the deployment of reliable, state-of-the-art AI systems.

Labeling Experience

Data Labeling Specialist – Sentiment & Intent Annotation

ProdigyTextClassification

I annotated multilingual text data for sentiment analysis and intent classification projects. My work contributed to the development of natural language understanding models supporting multiple languages. Automation scripts I created streamlined the cleaning and preparation of text data for annotation workflows. • Labeled text samples for positive, negative, and neutral sentiment. • Classified intent categories for chatbot and digital assistant use cases. • Utilized Labelbox and Prodigy for annotation and quality assurance. • Automated data cleaning with Python to increase annotation throughput.

I annotated multilingual text data for sentiment analysis and intent classification projects. My work contributed to the development of natural language understanding models supporting multiple languages. Automation scripts I created streamlined the cleaning and preparation of text data for annotation workflows. • Labeled text samples for positive, negative, and neutral sentiment. • Classified intent categories for chatbot and digital assistant use cases. • Utilized Labelbox and Prodigy for annotation and quality assurance. • Automated data cleaning with Python to increase annotation throughput.

2022 - Present

Data Labeling Specialist – Semantic Segmentation & LIDAR

CVAT3D SensorSegmentation

I performed semantic segmentation and LIDAR labeling to support autonomous driving datasets. This involved using specialized annotation tools to create precise labels for 3D sensor data. My work helped improve the perception and decision-making capabilities of self-driving systems. • Labeled LIDAR point clouds and segmented objects for autonomous vehicle AI. • Ensured quality and consistency according to project guidelines. • Identified edge cases and challenging scenarios in real-world data. • Contributed to successful model retraining and performance gains.

I performed semantic segmentation and LIDAR labeling to support autonomous driving datasets. This involved using specialized annotation tools to create precise labels for 3D sensor data. My work helped improve the perception and decision-making capabilities of self-driving systems. • Labeled LIDAR point clouds and segmented objects for autonomous vehicle AI. • Ensured quality and consistency according to project guidelines. • Identified edge cases and challenging scenarios in real-world data. • Contributed to successful model retraining and performance gains.

2022 - Present

Data Labeling Specialist – RLHF & Red Teaming

LabelboxTextRLHF

I led reinforcement learning from human feedback (RLHF) tasks for Large Language Models (LLMs), focusing on ranking model responses and evaluating their helpfulness and honesty. My work involved collaborating closely with machine learning engineers to refine annotation guidelines and improve data quality. I executed red teaming exercises to detect and mitigate model biases and conducted quality audits on large-scale data sets to ensure high accuracy. • Ranked LLM responses for relevance, helpfulness, and honesty. • Collaborated on annotation guideline development for clarity and consistency. • Performed red teaming for bias and safety validation in AI models. • Audited over 500,000 data points achieving a 99.5% accuracy rate.

I led reinforcement learning from human feedback (RLHF) tasks for Large Language Models (LLMs), focusing on ranking model responses and evaluating their helpfulness and honesty. My work involved collaborating closely with machine learning engineers to refine annotation guidelines and improve data quality. I executed red teaming exercises to detect and mitigate model biases and conducted quality audits on large-scale data sets to ensure high accuracy. • Ranked LLM responses for relevance, helpfulness, and honesty. • Collaborated on annotation guideline development for clarity and consistency. • Performed red teaming for bias and safety validation in AI models. • Audited over 500,000 data points achieving a 99.5% accuracy rate.

2022 - Present

Education

Work History