Florence Obunga

Key Skills

Software

CVAT

Labelbox

Label Studio

Scale AI

SuperAnnotate

V7 Labs

Top Subject Matter

No subject matter listed

Top Data Types

3D Sensor

Audio

Image

Text

Top Label Types

Bounding Box

Classification

Data Collection

Entity Ner Classification

Object Detection

Point Key Point

Segmentation

Freelancer Overview

I am a detail-oriented AI specialist with hands-on experience in data labeling, annotation, and AI training data workflows. My background includes annotating 3D LiDAR point cloud data for autonomous vehicle projects, generating and evaluating prompts for LLM outputs, and providing feedback to improve AI accuracy and tone. I’ve contributed to projects across computer vision and natural language processing domains, managed remote teams for large-scale data labeling, and have strong skills in Python scripting, dataset integration, and workflow optimization. My technical expertise, combined with a flexible and remote-ready work style, allows me to deliver high-quality, accurate training data for a variety of AI applications.

Labeling Experience

AI Image & Video Data Annotation Specialist

Scale AIImageBounding BoxPolygon

Worked on large-scale computer vision data annotation projects focused on training AI models for real-world visual recognition. Tasks included labeling objects in images and video frames using bounding boxes, polygons, and segmentation masks to identify vehicles, pedestrians, road signs, and environmental features.The project involved annotating thousands of images and validating dataset accuracy to support machine learning models used in autonomous driving systems. Strict quality control guidelines were followed, including multi-pass verification, adherence to labeling taxonomies, and consistency checks to ensure high-precision annotations suitable for AI model training.

Worked on large-scale computer vision data annotation projects focused on training AI models for real-world visual recognition. Tasks included labeling objects in images and video frames using bounding boxes, polygons, and segmentation masks to identify vehicles, pedestrians, road signs, and environmental features.The project involved annotating thousands of images and validating dataset accuracy to support machine learning models used in autonomous driving systems. Strict quality control guidelines were followed, including multi-pass verification, adherence to labeling taxonomies, and consistency checks to ensure high-precision annotations suitable for AI model training.

2024 - 2024

Education

Work History