Joseph B.

Key Skills

Software

Labelbox

CVAT

Top Subject Matter

Autonomous vehicle object detection

facial recognition

audio transcription/speech recognition

Top Data Types

Image

Video

Audio

Top Task Types

Segmentation

Bounding Box

Tracking

Transcription

Freelancer Overview

AI Training Specialist / Data Annotation Expert (Freelance AI Training Projects). Core strengths include Labelbox, YOLO, and CVAT. Education includes Bachelor of Science, The University of Alabama (2022). AI-training focus includes data types such as Image, Video, and Audio and labeling workflows including Segmentation, Bounding Box, and Tracking.

Labeling Experience

Image & Video Data Annotation and AI Training Support

VideoBounding Box

Worked on both image and video data annotation projects to support computer vision and AI model training. For image data, performed high-accuracy labeling including bounding boxes, segmentation masks, and classification across various datasets such as urban environments, people, vehicles, and general objects. For video data, annotated frame-by-frame objects, performed object tracking, temporal labeling, and activity recognition tasks to support motion-based AI models. Ensured consistency and quality by strictly following annotation guidelines, conducting self-review, and correcting labeling errors before submission. Utilized tools such as Labelbox, CVAT, and YOLO annotation formats. Collaborated with QA workflows to maintain dataset accuracy, reduce noise, and improve model performance.

2025 - Present

AI Training Specialist / Data Annotation Expert (Freelance AI Training Projects)

LabelboxImageSegmentationBounding Box

Freelance AI training and data annotation specialist providing high-quality labels for computer vision and speech/NLP datasets. Produced bounding box, polygon/segmentation, and object tracking annotations to support model training and quality assurance. Also labeled audio content through speech and audio transcription tasks while following strict annotation guidelines and deadlines. • Labeled objects, actions, and events for computer vision training • Conducted polygon/segmentation and tracking annotations for temporal consistency • Reviewed datasets for consistency, accuracy, and quality control • Collaborated with AI development teams to improve training data performance

2023 - Present

AI Content Review & Text Data Annotation

TextClassification

Worked on text annotation and AI content review projects to support natural language processing and machine learning systems. Responsible for reviewing, labeling, categorizing, and moderating digital text content according to detailed project guidelines and quality standards. Performed sentiment analysis, text classification, keyword tagging, and content evaluation tasks while maintaining high accuracy and consistency. Demonstrated strong written English communication skills, attention to detail, and the ability to work independently in remote environments. Collaborated with QA workflows to improve dataset reliability and support AI model training and evaluation processes within required deadlines.

2023 - 2025

Image Data Annotation & AI Training Support

ImageBounding Box

Worked on image data annotation projects to support machine learning and computer vision model training. Performed high-accuracy labeling using bounding boxes, segmentation masks, and image classification across diverse datasets including urban scenes, vehicles, people, and everyday objects. Ensured consistency and quality by following strict annotation guidelines, performing self-review, and correcting labeling errors before submission. Used tools such as Labelbox, CVAT, and YOLO-format annotation standards to prepare datasets for model training. Collaborated with QA pipelines to maintain dataset accuracy, reduce labeling noise, and improve model performance.

2023 - 2024

Multimodal Data Annotation and AI Training Support (Image, Video & Audio)

AudioTranscription

Worked on multimodal data annotation projects supporting AI and machine learning model training across image, video, and audio datasets. For image data, performed high-quality labeling including bounding boxes, segmentation masks, and classification across diverse categories such as people, vehicles, and real-world environments. For video data, completed frame-by-frame annotation, object tracking, temporal labeling, and activity recognition to support motion-based AI models. For audio data, performed transcription, speech labeling, speaker identification, audio tagging, and noise classification to improve speech and sound recognition systems. Ensured high accuracy and consistency by following strict annotation guidelines, performing quality checks, and correcting errors before final submission. Used tools such as Labelbox, CVAT, and YOLO formats while maintaining QA standards to improve dataset reliability and model performance.

2022 - 2024

Education

Work History