Juvon T.

Key Skills

Software

Labelbox

Other

Appen

Top Subject Matter

Computer Vision (Object Detection, Segmentation, Multi-object Tracking) and Audio Dataset Annotation

Computer Vision and Speech Recognition AI (Video Tracking and Audio Transcription)

Top Data Types

Image

Video

Audio

Top Task Types

Bounding Box

Segmentation

Classification

Tracking

Object Detection

Transcription

Freelancer Overview

AI Training Specialist — Scale AI (Remote). Core strengths include Labelbox and Other. Education includes Bachelor of Science, University of Central Florida (2022). AI-training focus includes data types such as Image, Video, and Audio and labeling workflows including Bounding Box, Segmentation, and Classification.

Labeling Experience

Image Annotation for Object Detection and Computer Vision AI Training

ImageBounding Box

This project involved large-scale image annotation for AI and computer vision model training. The primary scope included labeling static images using bounding boxes, polygon segmentation, and classification tags to support object detection and recognition systems. Tasks included identifying multiple objects within images, drawing precise bounding boxes around target objects, performing segmentation for complex shapes, and assigning accurate class labels based on project guidelines. The dataset included diverse real-world environments to improve model generalization and robustness. The project was conducted under strict quality standards, including multi-level annotation review, consistency checks, and validation against labeling guidelines. Emphasis was placed on accuracy, edge-case handling, and maintaining uniform annotation quality across large datasets used for machine learning training pipelines.

2023 - Present

AI Training Specialist — Scale AI (Remote)

LabelboxImageBounding BoxSegmentation

Delivered large-scale labeled datasets for AI and machine learning workflows across image, video, and audio modalities. Produced bounding boxes, polygon segmentation annotations, and classification labels to support object detection and related tasks. Performed frame-by-frame video annotation and multi-object tracking while enforcing labeling guidelines for training quality. • Used Labelbox for annotation workflow management and QA review • Supported YOLO-based object detection pipelines and validation • Collaborated with engineers and QA teams to reduce annotation errors • Maintained labeling accuracy and consistency across high-volume datasets

2023 - Present

Text Data Annotation for NLP Model Training and Classification Systems

TextClassification

This project involved large-scale text data annotation for Natural Language Processing (NLP) and machine learning model training. The scope included labeling and structuring unstructured text data for classification, entity recognition, and sentiment analysis tasks. Responsibilities included categorizing text into predefined classes, identifying and tagging named entities, and performing sentiment labeling (positive, negative, neutral) based on contextual meaning. The dataset included diverse textual inputs such as user-generated content, transcripts, and structured documentation used for training AI language models. Strict quality control measures were followed, including consistency checks, guideline compliance reviews, and validation of annotation accuracy. Emphasis was placed on maintaining high precision in entity recognition and classification tasks to ensure reliable model training outputs.

2023 - 2026

Video Object Tracking & Annotation for AI Training Models

VideoBounding Box

This project involved large-scale video data annotation for computer vision and AI model training. The scope included frame-by-frame object tracking, bounding box labeling, and temporal consistency annotation across multi-object video sequences. The primary goal was to generate high-quality labeled datasets suitable for training and improving machine learning models used in object detection and tracking systems. Tasks performed included identifying and labeling multiple objects per frame, maintaining consistent tracking IDs across video timelines, and ensuring accurate spatial and temporal annotations. The dataset included diverse real-world scenarios to improve model generalization. The project involved medium-to-large scale video datasets processed under strict annotation guidelines. Quality was maintained through multi-stage review processes, including self-verification, cross-checking for labeling consistency, and adherence to project-specific annotation rules. High emphasis was placed on precision, edge-case handling, and minimizing labeling errors to ensure training-ready datasets.

2022 - 2025

Data Annotation Associate — TechVision AI Solutions (Remote)

OtherObject DetectionTracking

Annotated datasets for computer vision and speech recognition AI projects. Created labels for object tracking and motion in video-based datasets while performing audio transcription, tagging, and classification for speech-related tasks. • Managed dataset organization, labeling standards, and metadata structuring • Maintained consistent annotation quality across multiple datasets and projects • Supported video object tracking and motion labeling workflows • Performed audio transcription and classification tasks

2022 - 2023

Education

Work History