Rodriguez Gabriel

Key Skills

Software

Labelbox

CVAT

Supervisely

Top Subject Matter

Machine Learning/Data Annotation

Object Detection and Classification

Supervised Learning Dataset Preparation

Top Data Types

Image

Text

Document

Top Task Types

Bounding Box

Classification

Freelancer Overview

AI Training Specialist. Core strengths include Labelbox, CVAT, and Supervisely. Education includes Bachelor of Science, University of Houston (2021) and Diploma, Houston Community College (2017). AI-training focus includes data types such as Image and labeling workflows including Bounding Box and Classification.

Labeling Experience

AI Training Specialist — Multimedia Data Annotation Project

ImageObject Detection

Worked as an AI Training Specialist responsible for annotating and preparing large-scale datasets used for machine learning and computer vision model development. Tasks included image, video, and audio labeling using structured annotation guidelines to ensure high dataset accuracy. Performed bounding box annotation, polygon segmentation, classification labeling, and object tracking across video frames. Conducted audio tagging and sound classification for supervised learning applications. Maintained consistency across datasets through quality assurance reviews and adherence to annotation standards. The project involved processing thousands of multimedia samples while maintaining high precision and guideline compliance. Collaborated with remote AI teams to improve annotation workflows and support model training performance.

Worked as an AI Training Specialist responsible for annotating and preparing large-scale datasets used for machine learning and computer vision model development. Tasks included image, video, and audio labeling using structured annotation guidelines to ensure high dataset accuracy. Performed bounding box annotation, polygon segmentation, classification labeling, and object tracking across video frames. Conducted audio tagging and sound classification for supervised learning applications. Maintained consistency across datasets through quality assurance reviews and adherence to annotation standards. The project involved processing thousands of multimedia samples while maintaining high precision and guideline compliance. Collaborated with remote AI teams to improve annotation workflows and support model training performance.

2023 - Present

AI Training Specialist

LabelboxImageBounding Box

As an AI Training Specialist, I led the annotation of images, videos, and audio datasets for machine learning projects. I utilized bounding boxes, segmentation masks, and classification labels to prepare high-quality datasets. Tracking methodologies and dataset quality assurance practices were integral to my workflow. • Annotated diverse multimedia datasets using Labelbox, CVAT, Supervisely, and Roboflow. • Delivered high-accuracy, guideline-compliant annotations for computer vision and audio tasks. • Performed object detection workflows and reviewed label consistency. • Collaborated with AI teams to refine annotation standards.

As an AI Training Specialist, I led the annotation of images, videos, and audio datasets for machine learning projects. I utilized bounding boxes, segmentation masks, and classification labels to prepare high-quality datasets. Tracking methodologies and dataset quality assurance practices were integral to my workflow. • Annotated diverse multimedia datasets using Labelbox, CVAT, Supervisely, and Roboflow. • Delivered high-accuracy, guideline-compliant annotations for computer vision and audio tasks. • Performed object detection workflows and reviewed label consistency. • Collaborated with AI teams to refine annotation standards.

2023 - Present

AI Training Specialist — Multimedia Data Annotation Project

VideoSegmentation

Performed video data annotation and labeling to support computer vision and machine learning model training. Responsibilities included frame-by-frame object detection, tracking, and classification across diverse video datasets following detailed annotation guidelines. Annotated moving objects using bounding boxes and polygon segmentation while maintaining temporal consistency across frames. Conducted object tracking to ensure accurate identification of targets throughout video sequences. Applied labeling standards to categorize activities, objects, and environmental elements relevant to AI model objectives. Processed large-scale video datasets while maintaining high accuracy and consistency through quality assurance checks, guideline compliance, and validation reviews. Collaborated within remote AI workflows to improve dataset reliability and optimize model training performance.

Performed video data annotation and labeling to support computer vision and machine learning model training. Responsibilities included frame-by-frame object detection, tracking, and classification across diverse video datasets following detailed annotation guidelines. Annotated moving objects using bounding boxes and polygon segmentation while maintaining temporal consistency across frames. Conducted object tracking to ensure accurate identification of targets throughout video sequences. Applied labeling standards to categorize activities, objects, and environmental elements relevant to AI model objectives. Processed large-scale video datasets while maintaining high accuracy and consistency through quality assurance checks, guideline compliance, and validation reviews. Collaborated within remote AI workflows to improve dataset reliability and optimize model training performance.

2023 - 2025

Data Annotation Associate

CVATImageBounding Box

I contributed as a Data Annotation Associate by labeling datasets for object detection and classification tasks. Audio transcription and sound labeling were also performed using structured schemas and project-specific guidelines. I took part in peer-review processes to ensure annotation accuracy and quality control. • Labeled images and audio data for machine learning purposes. • Applied structured annotation schemas to diverse datasets. • Supported model training initiatives through accurate tagging. • Participated in peer review for quality assurance.

I contributed as a Data Annotation Associate by labeling datasets for object detection and classification tasks. Audio transcription and sound labeling were also performed using structured schemas and project-specific guidelines. I took part in peer-review processes to ensure annotation accuracy and quality control. • Labeled images and audio data for machine learning purposes. • Applied structured annotation schemas to diverse datasets. • Supported model training initiatives through accurate tagging. • Participated in peer review for quality assurance.

2022 - 2023

Junior Data Labeling Assistant

SuperviselyImageClassification

As a Junior Data Labeling Assistant, I supported the preparation of datasets for supervised learning projects. My role involved categorizing visual and audio content while verifying annotation accuracy. I contributed to the organization and validation of datasets for AI model training. • Performed image and audio categorization with attention to detail. • Assisted in ensuring labeling consistency and quality. • Helped maintain organized, well-structured datasets. • Provided additional verification for quality assurance.

As a Junior Data Labeling Assistant, I supported the preparation of datasets for supervised learning projects. My role involved categorizing visual and audio content while verifying annotation accuracy. I contributed to the organization and validation of datasets for AI model training. • Performed image and audio categorization with attention to detail. • Assisted in ensuring labeling consistency and quality. • Helped maintain organized, well-structured datasets. • Provided additional verification for quality assurance.

2021 - 2022

Education

Work History