João Boligian

Key Skills

Software

AWS SageMaker

Appen

Clickworker

CloudFactory

Data Annotation Tech

Google Cloud Vertex AI

Label Studio

Remotasks

Scale AI

Toloka

Telus

Labelbox

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Computer Code Programming

Document

Top Task Types

Evaluation Rating

Text Generation

Text Summarization

Translation Localization

Freelancer Overview

With 2 years of experience in data labeling and AI training data preparation, I have honed skills in accurately annotating datasets for diverse industries, including healthcare, e-commerce, and autonomous systems. My expertise spans text, image, and video data labeling, utilizing advanced tools such as Labelbox, Amazon SageMaker, and CVAT. I’ve contributed to projects requiring complex classifications, such as natural language processing, computer vision, and sentiment analysis, ensuring data quality and consistency. My meticulous attention to detail and collaborative approach consistently drive successful AI model training outcomes.

Labeling Experience

Image Annotation for Autonomous Vehicles

LabelboxImageText GenerationObject Detection

Annotated a dataset of over 100,000 road images for training self-driving car models. Tasks included object detection for vehicles, pedestrians, and road signs, as well as semantic segmentation of lanes and road boundaries. Ensured high accuracy using QA protocols and iterative feedback loops, optimizing annotations for edge cases such as adverse weather and low-light conditions. The project significantly improved model performance in real-world scenarios, demonstrating expertise in advanced annotation techniques and industry-specific requirements.

Annotated a dataset of over 100,000 road images for training self-driving car models. Tasks included object detection for vehicles, pedestrians, and road signs, as well as semantic segmentation of lanes and road boundaries. Ensured high accuracy using QA protocols and iterative feedback loops, optimizing annotations for edge cases such as adverse weather and low-light conditions. The project significantly improved model performance in real-world scenarios, demonstrating expertise in advanced annotation techniques and industry-specific requirements.

2024 - 2024

Education

Work History