Zambrano Felix

Key Skills

Software

Labelbox

CVAT

Top Subject Matter

No subject matter listed

Top Data Types

Image

Video

Audio

Top Task Types

Bounding Box

Point Key Point

Classification

Tracking

Emotion Recognition

Audio Recording

Freelancer Overview

I am an experienced AI Training Specialist with three years of hands-on expertise in data labeling and annotation for machine learning projects. My work spans image, video, and audio annotation, including object detection, segmentation, classification, and transcription tasks. I excel at following detailed annotation guidelines, ensuring accuracy and consistency across large datasets, and conducting thorough quality assurance checks to support reliable model performance. I am highly proficient with industry-standard tools such as Labelbox, CVAT, Supervisely, YODO, and VGG Image Annotator, as well as data management using Excel and Google Sheets. My strong attention to detail and commitment to high-quality data preparation have enabled me to contribute effectively to AI model training and workflow optimization in diverse domains. I am fluent in both English and Spanish, allowing me to work efficiently in multilingual environments.

Labeling Experience

AI Training Data Annotation — Multi-Modal Dataset

LabelboxImageBounding BoxPoint Key Point

Annotated large-scale image, vedio, and audio datasets for machine learning training and evaluation. Performed object detection using bounding boxes, semantic segmentation, classification tagging, and audio transcription. Ensured strict adherence to annotation guidelines to maintain dataset accuracy and consistency. Conducted quality assurance reviews to identify labeling errors and improve dataset reliability. Contributed to the preparation of structured training data used to enhance AI model performance across computer vision and speech-related tasks.

Annotated large-scale image, vedio, and audio datasets for machine learning training and evaluation. Performed object detection using bounding boxes, semantic segmentation, classification tagging, and audio transcription. Ensured strict adherence to annotation guidelines to maintain dataset accuracy and consistency. Conducted quality assurance reviews to identify labeling errors and improve dataset reliability. Contributed to the preparation of structured training data used to enhance AI model performance across computer vision and speech-related tasks.

2022

AI Training Data Annotation — Multi-Modal Dataset

CVATAudioBounding BoxPoint Key Point

Annotated large-scale audio datasets for machine learning and AI model training. Performed tasks including transcription, speaker identification, emotion recognition, and sound event labeling. Ensured high-quality annotations by following strict guidelines and performing quality checks for clarity, accuracy, and consistency. Prepared structured audio training data used to improve speech recognition, natural language understanding, and audio-based AI models.

Annotated large-scale audio datasets for machine learning and AI model training. Performed tasks including transcription, speaker identification, emotion recognition, and sound event labeling. Ensured high-quality annotations by following strict guidelines and performing quality checks for clarity, accuracy, and consistency. Prepared structured audio training data used to improve speech recognition, natural language understanding, and audio-based AI models.

2024 - 2025

AI Training Data Annotation — Multi-Modal Dataset

LabelboxVideoBounding BoxPoint Key Point

Annotated large-scale video datasets for machine learning and computer vision model training. Performed frame-by-frame labeling using bounding boxes and segmentation to identify objects, human activities, and motion patterns. Conducted action recognition and object tracking across continuous video sequences to ensure temporal consistency. Applied strict annotation guidelines and performed quality assurance reviews to maintain high accuracy and dataset reliability. Contributed structured, high-quality video training data to improve model performance in detection, tracking, and behavior analysis tasks.

Annotated large-scale video datasets for machine learning and computer vision model training. Performed frame-by-frame labeling using bounding boxes and segmentation to identify objects, human activities, and motion patterns. Conducted action recognition and object tracking across continuous video sequences to ensure temporal consistency. Applied strict annotation guidelines and performed quality assurance reviews to maintain high accuracy and dataset reliability. Contributed structured, high-quality video training data to improve model performance in detection, tracking, and behavior analysis tasks.

2023 - 2024

Education

Work History