Joseph Paul

Key Skills

Software

Mercor

Other

Labelbox

Clickworker

Top Subject Matter

LLM Evaluation and Data Labeling

Video and image labeling and annotation.

Regulatory Compliance & Risk Analysis

Top Data Types

Image

Video

Text

Document

Top Task Types

Question Answering

Bounding Box

Text Generation

Transcription

Prompt Response Writing SFT

Classification

Freelancer Overview

Data Annotator. Brings 47+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Mercor. Education includes Certificate, Rutgers University (2022) and Bachelor of Arts, Rutgers, The State University of New Jersey – New Brunswick (1980). AI-training focus includes data types such as Image and labeling workflows including Evaluation and Rating.

Labeling Experience

English Data Annotation Expert

VideoText Generation

I worked specifically as a data annotator, contributing to the preparation of high-quality labeled datasets used to train machine learning models. The scope of the project involved labeling multimodal data, including images, videos, and text, to support both computer vision and natural language processing tasks. I followed detailed annotation guidelines, used specialized labeling platforms, and ensured that all annotations aligned with predefined class definitions, project objectives, and strict turnaround timelines. My responsibilities focused on performing specific data labeling tasks with high precision and consistency. For image and video data, I drew accurate bounding boxes around objects such as vehicles and pedestrians, ensuring proper classification and alignment with annotation rules. I also carried out image classification and, where required, segmentation tasks. On the text side, I handled sentiment analysis, named entity recognition (NER), and prompt-response evaluation, carefully applying labeling criteria to maintain clarity and reduce ambiguity. Throughout all tasks, I ensured consistency in labeling decisions to support reliable model training. The project was large-scale, involving hundreds of thousands of data points and collaboration within a distributed team of annotators. I managed my workload efficiently to meet daily and weekly targets while maintaining accuracy. Working at this scale required strong attention to detail, time management, and the ability to adapt quickly to guideline updates and project changes. To maintain high quality standards, I adhered to rigorous quality assurance measures. I regularly referenced gold-standard datasets, participated in inter-annotator agreement (IAA) checks, and conducted thorough self-reviews before submission. I incorporated feedback from quality assurance teams and engaged in calibration sessions to stay aligned with project expectations. By consistently focusing on accuracy, completeness, and guideline compliance, I ensured that my contributions met the quality benchmarks required for effective AI model training.

I worked specifically as a data annotator, contributing to the preparation of high-quality labeled datasets used to train machine learning models. The scope of the project involved labeling multimodal data, including images, videos, and text, to support both computer vision and natural language processing tasks. I followed detailed annotation guidelines, used specialized labeling platforms, and ensured that all annotations aligned with predefined class definitions, project objectives, and strict turnaround timelines. My responsibilities focused on performing specific data labeling tasks with high precision and consistency. For image and video data, I drew accurate bounding boxes around objects such as vehicles and pedestrians, ensuring proper classification and alignment with annotation rules. I also carried out image classification and, where required, segmentation tasks. On the text side, I handled sentiment analysis, named entity recognition (NER), and prompt-response evaluation, carefully applying labeling criteria to maintain clarity and reduce ambiguity. Throughout all tasks, I ensured consistency in labeling decisions to support reliable model training. The project was large-scale, involving hundreds of thousands of data points and collaboration within a distributed team of annotators. I managed my workload efficiently to meet daily and weekly targets while maintaining accuracy. Working at this scale required strong attention to detail, time management, and the ability to adapt quickly to guideline updates and project changes. To maintain high quality standards, I adhered to rigorous quality assurance measures. I regularly referenced gold-standard datasets, participated in inter-annotator agreement (IAA) checks, and conducted thorough self-reviews before submission. I incorporated feedback from quality assurance teams and engaged in calibration sessions to stay aligned with project expectations. By consistently focusing on accuracy, completeness, and guideline compliance, I ensured that my contributions met the quality benchmarks required for effective AI model training.

2026 - 2026

Data Annotator

MercorImage

As a Data Annotator at Mercor, I reviewed and evaluated responses generated by large language models (LLMs). My role also involved annotating different objects presented visually in image data. I worked to ensure high-quality data labeling for AI and machine learning applications. • Evaluated large language model (LLM) responses for accuracy and relevance. • Annotated images containing various objects for computer vision efforts. • Ensured high data quality through consistent application of annotation standards. • Collaborated with project managers to meet annotation volume and quality targets.

As a Data Annotator at Mercor, I reviewed and evaluated responses generated by large language models (LLMs). My role also involved annotating different objects presented visually in image data. I worked to ensure high-quality data labeling for AI and machine learning applications. • Evaluated large language model (LLM) responses for accuracy and relevance. • Annotated images containing various objects for computer vision efforts. • Ensured high data quality through consistent application of annotation standards. • Collaborated with project managers to meet annotation volume and quality targets.

2025 - 2026

Education

Work History