Breylon Riley

Key Skills

Software

Appen

CVAT

Data Annotation Tech

OpenCV AI Kit (OAK)

Remotasks

Scale AI

Top Subject Matter

LLM evaluation in English

Medical Imaging

Image Classification

Top Data Types

Audio

Computer Code Programming

Medical Dicom

Top Task Types

Audio Recording

Bounding Box

Classification

Computer Programming Coding

Prompt Response Writing SFT

Freelancer Overview

I am a skilled data professional with extensive experience in data labeling, AI training, and model evaluation across diverse industries. My background spans computational biophysics, clinical data management, and programming, allowing me to contribute to improving AI model performance for over 10 companies, including those in healthcare (medical image diagnosis), logistics (inventory management), and more. I've successfully developed and optimized data pipelines, from web scraping for competitive analysis to building sentiment analysis tools that drive business insights. I have a strong command of Python, R, SQL, SAS, and C++, and I’ve worked with advanced AI frameworks to refine large language models (LLMs) and enhance their functionality. My experience includes managing complex datasets and ensuring the highest quality AI training data. Currently, I’m pursuing a PhD in Physics at Duke University, which further strengthens my analytical and problem-solving skills. This unique blend of academic rigor and hands-on experience allows me to deliver results in AI, data science, and beyond.

Labeling Experience

AI Trainer

Data Annotation TechImageBounding BoxEntity Ner Classification

In this project, I worked independently to annotate large datasets for training AI models used in autonomous farm vehicles. I performed a range of data labeling tasks, including Bounding Box Annotation for object detection, Entity Recognition (NER) for classifying text-based data, Image Segmentation to distinguish crops from weeds, and Image Classification to categorize field conditions. The project involved labeling over 50,000 images from diverse agricultural environments, including fields and orchards. I followed strict quality measures, including consistency checks, regular validation, and iterative refinement based on model feedback, to ensure the accuracy and effectiveness of the labeled data. My efforts contributed directly to enhancing the vehicle's ability to navigate fields, detect obstacles, and perform precision tasks like planting and harvesting. Ultimately, the project improved operational efficiency and reduced resource waste for farm operators.

In this project, I worked independently to annotate large datasets for training AI models used in autonomous farm vehicles. I performed a range of data labeling tasks, including Bounding Box Annotation for object detection, Entity Recognition (NER) for classifying text-based data, Image Segmentation to distinguish crops from weeds, and Image Classification to categorize field conditions. The project involved labeling over 50,000 images from diverse agricultural environments, including fields and orchards. I followed strict quality measures, including consistency checks, regular validation, and iterative refinement based on model feedback, to ensure the accuracy and effectiveness of the labeled data. My efforts contributed directly to enhancing the vehicle's ability to navigate fields, detect obstacles, and perform precision tasks like planting and harvesting. Ultimately, the project improved operational efficiency and reduced resource waste for farm operators.

2023 - 2024

Education

Work History