James Berneou

Key Skills

Software

Appen

Top Subject Matter

No subject matter listed

Top Data Types

Image

Top Label Types

Bounding Box

Polygon

Freelancer Overview

I bring over five years of hands-on experience in data labeling and AI training data development, with a strong focus on accuracy, consistency, and scalable annotation workflows. As a Data Annotation Specialist at Appen and previously an AI Data Associate at Lionbridge AI, I’ve worked extensively across multimedia datasets—including image, video, and audio—supporting machine learning model training in diverse domains. My responsibilities have included large-scale data annotation, pairwise comparisons, dataset preprocessing, and rigorous quality assurance checks to ensure high-fidelity labeled data. I also contributed to establishing tagging standards that reduced labeling errors by 20%, directly improving downstream model performance. What sets me apart is my combination of technical foundation and operational efficiency. With a background in Computer Science and practical experience using Python, SQL, Excel, and various data labeling tools, I understand both the annotation layer and the model training lifecycle it supports. I’ve consistently improved workflows by recommending automation tools and best practices for data collection and preprocessing. My experience spans data evaluation, quality control, and pipeline optimization, allowing me to contribute not just as a labeler, but as a data quality advocate focused on delivering training datasets that are accurate, scalable, and model-ready.

Labeling Experience

Multimedia Annotation for Computer Vision Models (Appen)

AppenImageBounding BoxPolygon

This project involved large-scale multimedia annotation to support the training and evaluation of computer vision models. I performed detailed image and video labeling tasks including bounding box annotation, polygon segmentation, object tracking across video frames, and multi-class object classification. The datasets included diverse real-world environments requiring precise object localization, attribute tagging, and frame-by-frame consistency. The project spanned tens of thousands of annotated images and video sequences. I adhered strictly to taxonomy guidelines and labeling protocols to ensure high inter-annotator agreement and dataset consistency. In addition to annotation, I conducted quality assurance checks, peer reviews, and pairwise comparisons to evaluate model predictions and improve dataset reliability. I also contributed suggestions to streamline workflows and improve annotation efficiency while maintaining high accuracy standards.

This project involved large-scale multimedia annotation to support the training and evaluation of computer vision models. I performed detailed image and video labeling tasks including bounding box annotation, polygon segmentation, object tracking across video frames, and multi-class object classification. The datasets included diverse real-world environments requiring precise object localization, attribute tagging, and frame-by-frame consistency. The project spanned tens of thousands of annotated images and video sequences. I adhered strictly to taxonomy guidelines and labeling protocols to ensure high inter-annotator agreement and dataset consistency. In addition to annotation, I conducted quality assurance checks, peer reviews, and pairwise comparisons to evaluate model predictions and improve dataset reliability. I also contributed suggestions to streamline workflows and improve annotation efficiency while maintaining high accuracy standards.

2024

Education

Work History