Esther Ngigi

Key Skills

Software

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Image

Video

Top Label Types

Bounding Box

Data Collection

Segmentation

Transcription

Freelancer Overview

I am a detail-oriented data annotator with hands-on experience supporting AI and machine learning projects through high-quality data labeling and validation. My background includes annotating and validating image, video, LiDAR, voice, and text datasets for computer vision and NLP applications, with a proven track record of maintaining accuracy and consistency in remote, fast-paced environments. I am skilled in using tools like Python, Excel, Google Sheets, and Jupyter Notebook, and have contributed to projects such as speech transcription, audio validation, image classification, and LiDAR annotation for autonomous systems. I am passionate about leveraging my analytical skills, attention to detail, and collaborative mindset to deliver reliable training data that drives intelligent AI solutions.

Labeling Experience

data annotation

Scale AIVideoBounding BoxSegmentation

Performed high-precision data labeling for autonomous vehicle perception systems by annotating LiDAR point clouds, camera images, and video sequences. Created accurate 2D and 3D bounding boxes, cuboids, and semantic/instance segmentation for vehicles, pedestrians, cyclists, road signs, and lane markings. Conducted frame-by-frame object tracking and classification to ensure temporal consistency across video data. Followed strict quality assurance guidelines and project specifications to deliver reliable, production-ready datasets that improved machine learning model performance and real-world driving scenario detection.

Performed high-precision data labeling for autonomous vehicle perception systems by annotating LiDAR point clouds, camera images, and video sequences. Created accurate 2D and 3D bounding boxes, cuboids, and semantic/instance segmentation for vehicles, pedestrians, cyclists, road signs, and lane markings. Conducted frame-by-frame object tracking and classification to ensure temporal consistency across video data. Followed strict quality assurance guidelines and project specifications to deliver reliable, production-ready datasets that improved machine learning model performance and real-world driving scenario detection.

2021 - 2021

Education

Work History