Israel Babalola

Key Skills

Software

Labelbox

CVAT

Roboflow

SuperAnnotate

Top Subject Matter

Data Annotations

Search Categorization

Image, Text and video annotation

Top Data Types

Text

Image

Document

Top Task Types

Classification

Bounding Box

Segmentation

Question Answering

Data Collection

Freelancer Overview

I am an AI Data Annotation and Evaluation Specialist with over 5 years of experience working across image, video, text, and audio datasets. I have annotated over 100,000 data points with 98% accuracy, including large-scale search categorization projects that improved NLP model performance. I am proficient with tools such as Labelbox, CVAT, and Doccano, and I consistently deliver high-quality, guideline-compliant data. In addition to annotation, I support AI system improvement through testing and evaluation. At Hybrid Workforce Solutions, I contribute to enhancing Upwork’s Uma AI by identifying bugs, analyzing outputs, and flagging inconsistencies.

Labeling Experience

Data Annotation Specialist

Image

Performed data evaluation and model error identification for the Upwork Uma AI system to improve computer vision and AI functionality. Systematically tested, analyzed, and reported inconsistencies and edge cases as part of ongoing quality improvement processes. Collaborated with remote teams to document and track issues affecting AI system performance. • Tested AI model outputs with structured assessment protocols • Documented errors and inconsistencies for development teams • Improved model performance through iterative bug reporting • Supported large-scale AI projects for system reliability and robustness

Performed data evaluation and model error identification for the Upwork Uma AI system to improve computer vision and AI functionality. Systematically tested, analyzed, and reported inconsistencies and edge cases as part of ongoing quality improvement processes. Collaborated with remote teams to document and track issues affecting AI system performance. • Tested AI model outputs with structured assessment protocols • Documented errors and inconsistencies for development teams • Improved model performance through iterative bug reporting • Supported large-scale AI projects for system reliability and robustness

2026 - Present

AI Data Annotation Specialist (NLP – Search Categorization Project)

LabelboxTextClassification

Responsible for annotating and categorizing over 100,000 search queries into predefined categories to enhance NLP model training and search relevance. Maintained strict adherence to detailed annotation guidelines while ensuring high consistency and quality benchmarks. Contributed to structured datasets for search optimization and user intent classification in AI-driven systems. • Categorized queries into 17 predefined classes using Labelbox software • Achieved 98% accuracy across high-volume annotation assignments • Focused on clear intent categorization and quality guideline compliance • Supported scalability for global AI data teams and large-scale NLP initiatives

Responsible for annotating and categorizing over 100,000 search queries into predefined categories to enhance NLP model training and search relevance. Maintained strict adherence to detailed annotation guidelines while ensuring high consistency and quality benchmarks. Contributed to structured datasets for search optimization and user intent classification in AI-driven systems. • Categorized queries into 17 predefined classes using Labelbox software • Achieved 98% accuracy across high-volume annotation assignments • Focused on clear intent categorization and quality guideline compliance • Supported scalability for global AI data teams and large-scale NLP initiatives

2025 - Present

Education

Work History