Emmanuel Onalo

Key Skills

Software

Appen

Clickworker

Google Cloud Vertex AI

Remotasks

Telus

Scale AI

CVAT

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Image

Text

Top Task Types

Action Recognition

Classification

Computer Programming Coding

Prompt Response Writing SFT

Question Answering

Freelancer Overview

Experienced AI Data Annotation Specialist with a strong background in training AI models through high-quality data labeling across computer vision, NLP, and autonomous systems. Skilled in image, video, and text annotation with expertise in bounding boxes, semantic segmentation, object tracking, and OCR annotation. Proficient in industry-leading tools like CVAT, Labelbox, Supervisely, and Amazon SageMaker Ground Truth to deliver precise datasets for machine learning and AI model development. I have contributed to autonomous vehicle perception, medical AI diagnostics, and e-commerce AI solutions, ensuring top-tier annotation quality that enhances model performance. With a deep understanding of AI training workflows, I have optimized annotation guidelines, improved dataset accuracy by 30%, and collaborated closely with AI engineers to refine model training strategies. Whether working on self-driving technology, healthcare imaging, or retail analytics, I bring meticulous attention to detail, problem-solving skills, and a results-driven approach to every project.

Labeling Experience

High-Precision Data Labeling for Autonomous Vehicle AI Models

CVATImageBounding BoxPolygon

Annotated 50,000+ road scene images and videos for an AI-powered autonomous driving system, improving object recognition accuracy by 30%. Conducted bounding box, polygon segmentation, and object tracking to train models for vehicle detection, pedestrian safety, and traffic sign recognition. Ensured high data quality and annotation consistency, collaborating with AI engineers to refine dataset labeling guidelines. Reviewed and corrected low-confidence labels, significantly reducing false positives in real-time vehicle perception models. Leveraged AI-assisted annotation tools to enhance workflow efficiency and optimize training datasets for next-generation self-driving technology.

Annotated 50,000+ road scene images and videos for an AI-powered autonomous driving system, improving object recognition accuracy by 30%. Conducted bounding box, polygon segmentation, and object tracking to train models for vehicle detection, pedestrian safety, and traffic sign recognition. Ensured high data quality and annotation consistency, collaborating with AI engineers to refine dataset labeling guidelines. Reviewed and corrected low-confidence labels, significantly reducing false positives in real-time vehicle perception models. Leveraged AI-assisted annotation tools to enhance workflow efficiency and optimize training datasets for next-generation self-driving technology.

2020 - 2023

Education

Work History