Moses Onyango

Key Skills

Software

Clickworker

CloudFactory

CVAT

Hasty

Labelbox

Remotasks

Sama

Toloka

Telus

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

3D Sensor

Audio

Video

Top Task Types

Bounding Box

Fine Tuning

RLHF

Freelancer Overview

I have over four years of experience in data labeling, video annotation, transcription, and AI training data preparation, working on diverse projects that require precision and strong attention to detail. My expertise spans image, video, and text-based annotation, including object tracking, classification, segmentation, and quality assurance. I’ve contributed to several large-scale AI model training projects, ensuring data accuracy and consistency that support the development of advanced machine learning systems. My strengths lie in my ability to understand complex labeling guidelines, maintain high-quality standards, and meet tight deadlines while handling large data volumes. I am proficient with various annotation tools and platforms and have a solid understanding of LLM training workflows and NLP data preparation. My commitment to accuracy, focus, and continuous learning enables me to deliver top-tier results that enhance AI performance and reliability.

Labeling Experience

3D video Annotation

SamaVideoBounding Box

I worked on a video annotation project focused on training AI models to recognize and track object movements in real-world environments. My main tasks included labeling and tracking objects frame by frame using bounding boxes, ensuring accuracy and consistency across sequences. I carefully followed annotation guidelines to identify correct object classes, correct mislabeled frames, and maintain uniformity in labeling styles. The project involved annotating hundreds of short video clips, with regular quality checks to ensure over 98% accuracy. My work contributed to enhancing object detection and motion tracking capabilities for robotics and computer vision applications.

I worked on a video annotation project focused on training AI models to recognize and track object movements in real-world environments. My main tasks included labeling and tracking objects frame by frame using bounding boxes, ensuring accuracy and consistency across sequences. I carefully followed annotation guidelines to identify correct object classes, correct mislabeled frames, and maintain uniformity in labeling styles. The project involved annotating hundreds of short video clips, with regular quality checks to ensure over 98% accuracy. My work contributed to enhancing object detection and motion tracking capabilities for robotics and computer vision applications.

2021 - 2025

Education

Work History