Leonardo Ortega

Key Skills

Software

Internal/Proprietary Tooling

CVAT

Top Subject Matter

No subject matter listed

Top Data Types

3D Sensor

Image

Video

Top Task Types

Computer Programming Coding

Freelancer Overview

I have hands-on experience in data labeling and AI training data processes, with a strong focus on accuracy, consistency, and efficiency. My work has included annotating text, images, and video datasets for machine learning models across diverse industries such as healthcare, e-commerce, and autonomous vehicles. I am well-versed in tools like Labelbox, Scale AI, and CVAT, and familiar with annotation techniques such as bounding boxes, semantic segmentation, named entity recognition, and sentiment analysis. My ability to follow complex guidelines and adapt to evolving project requirements ensures high-quality data output that supports robust model training. What sets me apart is my attention to detail, quality control mindset, and ability to identify and resolve edge cases that may affect model performance. I have collaborated with cross-functional teams, including data scientists and engineers, to improve labeling efficiency and develop feedback loops for continuous annotation refinement. This collaborative and iterative approach, combined with a commitment to ethical AI development, has allowed me to contribute to scalable, bias-aware AI systems.

Labeling Experience

Image and Text Annotation for Multimodal AI Models

CVATImagePolygon

I participated in a large-scale data labeling project focused on multimodal datasets for autonomous vehicles, e-commerce product classification, and healthcare diagnostics. My responsibilities included drawing bounding boxes and polygons for object detection, performing Named Entity Recognition (NER) on medical and retail texts, and sentiment analysis of user feedback. The project involved thousands of data samples and required high accuracy, adherence to detailed guidelines, and regular quality checks using gold standard data. I consistently met or exceeded quality benchmarks while collaborating with QA teams to refine labeling strategies and resolve ambiguities.

I participated in a large-scale data labeling project focused on multimodal datasets for autonomous vehicles, e-commerce product classification, and healthcare diagnostics. My responsibilities included drawing bounding boxes and polygons for object detection, performing Named Entity Recognition (NER) on medical and retail texts, and sentiment analysis of user feedback. The project involved thousands of data samples and required high accuracy, adherence to detailed guidelines, and regular quality checks using gold standard data. I consistently met or exceeded quality benchmarks while collaborating with QA teams to refine labeling strategies and resolve ambiguities.

2024

Education

Work History