Chrispine Ndegwa

Key Skills

Software

CloudFactory

Remotasks

Sama

Scale AI

Other

Top Subject Matter

No subject matter listed

Top Data Types

3D Sensor

Text

Video

Top Task Types

Bounding Box

Cuboid

Polygon

Segmentation

Text Generation

Freelancer Overview

I have hands-on experience in data labeling and AI training, with a strong focus on LiDAR annotation for autonomous vehicle systems. At Sama Source EPZ Kenya, I worked on complex 2D and 3D LiDAR datasets involving vulnerable road users (VRUs), vehicles, and multi-camera environments. I annotated both single-frame and sequential data using tools like TV Fisher and FC cameras, ensuring high-quality, model-ready outputs that supported the development of safer autonomous driving technologies. Additionally, I have worked part-time with Remotask and Avala AI, further refining my skills in image, video, and sensor data annotation across diverse AI training projects. These experiences have enhanced my attention to detail, accuracy, and understanding of machine learning data workflows. Beyond traditional data labeling, I have a growing interest in generative AI (Gen AI) applications. I am actively exploring how models like GPT can improve content generation, automate repetitive tasks in annotation, and support data augmentation for more robust training sets. This combination of hands-on annotation work, exposure to varied platforms, and a forward-thinking approach to integrating Gen AI makes me a valuable contributor in the AI training data space.

Labeling Experience

Avala A.i

Other3D SensorBounding BoxPolygon

At Avala AI, I contributed to the annotation of various AI training datasets, focusing on enhancing machine learning models for autonomous systems. My responsibilities included labeling images, videos, and sensor data for multiple AI applications, ensuring high accuracy and consistency in data quality. I specifically worked on annotating objects, road users, and environmental features in sensor data, which helped in the development of advanced perception algorithms. My tasks required a keen attention to detail, familiarity with AI model requirements, and the ability to meet strict deadlines while maintaining data integrity.

At Avala AI, I contributed to the annotation of various AI training datasets, focusing on enhancing machine learning models for autonomous systems. My responsibilities included labeling images, videos, and sensor data for multiple AI applications, ensuring high accuracy and consistency in data quality. I specifically worked on annotating objects, road users, and environmental features in sensor data, which helped in the development of advanced perception algorithms. My tasks required a keen attention to detail, familiarity with AI model requirements, and the ability to meet strict deadlines while maintaining data integrity.

2023 - 2024

Data Annotation Specialist

SamaVideoBounding BoxPolygon

I was part of a large-scale LiDAR data annotation project aimed at training autonomous vehicle perception systems, working with Sama Source EPZ Kenya and part-time through Remotask and Avala AI. The project involved annotating over 50,000 frames of 2D and 3D LiDAR data, focusing on the accurate labeling of vehicles, pedestrians, cyclists, and static infrastructure in diverse driving environments. I worked with both single-frame and sequence-based data using multi-camera setups (TV Fisher and FC cameras). My tasks included point cloud segmentation, bounding box annotation, trajectory tracking, and quality verification in compliance with strict accuracy and consistency standards. This role demanded a high level of attention to detail, understanding of spatial data, and collaboration with QA teams to maintain over 95% annotation accuracy. Additionally, I began exploring how generative AI (Gen AI) could enhance efficiency in data labeling and contribute to workflow automation.

I was part of a large-scale LiDAR data annotation project aimed at training autonomous vehicle perception systems, working with Sama Source EPZ Kenya and part-time through Remotask and Avala AI. The project involved annotating over 50,000 frames of 2D and 3D LiDAR data, focusing on the accurate labeling of vehicles, pedestrians, cyclists, and static infrastructure in diverse driving environments. I worked with both single-frame and sequence-based data using multi-camera setups (TV Fisher and FC cameras). My tasks included point cloud segmentation, bounding box annotation, trajectory tracking, and quality verification in compliance with strict accuracy and consistency standards. This role demanded a high level of attention to detail, understanding of spatial data, and collaboration with QA teams to maintain over 95% annotation accuracy. Additionally, I began exploring how generative AI (Gen AI) could enhance efficiency in data labeling and contribute to workflow automation.

2023 - 2024

Education

Work History