Emily Mate

Key Skills

Software

Appen

Clickworker

CloudFactory

CVAT

Labelbox

Lionbridge

Mindrift

OneForma

Remotasks

Sama

Scale AI

Toloka

Telus

Top Subject Matter

No subject matter listed

Top Data Types

Image

Text

Video

Top Task Types

Bounding Box

Classification

Computer Programming Coding

Cuboid

Data Collection

Freelancer Overview

I have hands on experience in data labeling and AI training data preparation, with a focus on accuracy, consistency, and efficiency. My work has included annotating diverse data types such as text, images, and structured datasets to support the development of machine learning models. I am skilled in applying detailed labeling guidelines, maintaining high-quality standards, and using annotation tools to deliver clean, structured datasets. I also understand the importance of data quality in improving model performance and can quickly adapt to new labeling requirements or project needs. What sets me apart is my attention to detail, problem-solving mindset, and strong organizational skills. I combine speed with precision, ensuring that large volumes of data are processed without compromising accuracy. In addition, I bring a background in data analysis, which helps me see beyond labeling tasks to how the data contributes to broader AI and machine learning goals. I am motivated to support innovative projects that rely on reliable training data and continuously sharpen my technical and analytical skills to grow in the AI space.

Labeling Experience

Data annotation

Scale AIVideoBounding BoxPolygon

I worked on the Llama LSS autonomous vehicle project through Remotask, which focused on creating high-quality 3D training data for self-driving technology. The scope of the project involved labeling LiDAR point clouds and 3D environments to help autonomous systems accurately detect and understand vehicles, pedestrians, traffic signs, and other road features. This data was crucial for training AI to make safe and reliable driving decisions in real-world conditions. My specific tasks included 3D cuboid annotation, object tracking across frames, and fine-grained labeling of road elements within large-scale LiDAR scenes. The project size was extensive, requiring consistent labeling of thousands of objects across multiple sequences while adhering to strict deadlines. To ensure quality, I followed detailed project guidelines, regularly cross-checked my work, and maintained accuracy scores above the required threshold. I took pride in balancing speed and precision, ensuring the datasets met

I worked on the Llama LSS autonomous vehicle project through Remotask, which focused on creating high-quality 3D training data for self-driving technology. The scope of the project involved labeling LiDAR point clouds and 3D environments to help autonomous systems accurately detect and understand vehicles, pedestrians, traffic signs, and other road features. This data was crucial for training AI to make safe and reliable driving decisions in real-world conditions. My specific tasks included 3D cuboid annotation, object tracking across frames, and fine-grained labeling of road elements within large-scale LiDAR scenes. The project size was extensive, requiring consistent labeling of thousands of objects across multiple sequences while adhering to strict deadlines. To ensure quality, I followed detailed project guidelines, regularly cross-checked my work, and maintained accuracy scores above the required threshold. I took pride in balancing speed and precision, ensuring the datasets met

2021 - 2024

Education

Work History