Edwin Kinyua

Key Skills

Software

Appen

Top Subject Matter

No subject matter listed

Top Data Types

Image

Top Task Types

Bounding Box

Polygon

Segmentation

Freelancer Overview

I am a detail-oriented Data Annotation Specialist with hands-on experience supporting AI and LLM training projects, particularly in scientific and biology domains. My background includes precise data labeling, chain-of-thought (CoT) development, and rigorous quality assurance for projects involving genetics, physiology, and molecular biology. I excel at annotating and validating AI-generated content, identifying inconsistencies, and ensuring high-quality, domain-specific datasets. I am proficient with annotation platforms, LaTeX for scientific documentation, and collaborative tools like Microsoft Office and Google Workspace. My strong scientific reasoning and attention to detail allow me to deliver accurate, structured outputs that support model improvement and reliable AI training data. I am available for remote and project-based opportunities in data annotation, AI training, and biology expert roles.

Labeling Experience

image labelling-Vehicle Object Detection

AppenImageBounding BoxPolygon

Contributed to a large-scale vehicle image annotation project focused on training computer vision models for object detection and classification. The project involved labeling thousands of real-world traffic and roadway images containing various vehicle types, including cars, buses, trucks, motorcycles, and emergency vehicles. Primary responsibilities included drawing precise bounding boxes and polygons around vehicles, classifying vehicle categories, and ensuring accurate differentiation between overlapping and partially occluded objects. Special attention was given to edge cases such as low-light conditions, motion blur, and crowded urban traffic scenes.

Contributed to a large-scale vehicle image annotation project focused on training computer vision models for object detection and classification. The project involved labeling thousands of real-world traffic and roadway images containing various vehicle types, including cars, buses, trucks, motorcycles, and emergency vehicles. Primary responsibilities included drawing precise bounding boxes and polygons around vehicles, classifying vehicle categories, and ensuring accurate differentiation between overlapping and partially occluded objects. Special attention was given to edge cases such as low-light conditions, motion blur, and crowded urban traffic scenes.

2022 - 2023

Education

Work History