Nyakundi Zaddock

Key Skills

Software

Appen

CVAT

Labelbox

LabelImg

Roboflow

Internal/Proprietary Tooling

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Image

Text

Video

Top Label Types

Bounding Box

Cuboid

Polygon

Polyline

Prompt Response Writing SFT

Question Answering

Red Teaming

Tracking

Transcription

Freelancer Overview

I am an experienced AI Training Specialist with over four years in data labeling, annotation, and quality assurance for computer vision and NLP projects. My background includes managing large-scale datasets, performing detailed image, video, audio, and text annotation, and improving model accuracy through careful curation and QA processes. I am proficient with industry-standard tools like Labelbox, YOLO, Roboflow, and CVAT, and have experience collaborating with cross-functional teams to deliver high-quality, scalable AI solutions. My work spans training and evaluating object detection models, conducting dataset augmentation, and ensuring annotation consistency, with notable contributions to projects in areas such as surveillance, retail analytics, and automation. I am committed to delivering precise, reliable training data that drives the success of AI models.

Labeling Experience

Autonomous Object Detection Dataset Annotation for Computer Vision Models

LabelboxImageBounding BoxSegmentation

Annotated and curated large-scale image and video datasets for object detection and tracking models used in real-world computer vision applications. Responsibilities included drawing accurate bounding boxes, polygon segmentation, and object tracking across video frames for multiple object classes. Managed dataset preprocessing, augmentation, and version control using Roboflow while ensuring annotation consistency through detailed guideline adherence. Performed quality assurance reviews and inter-annotator agreement checks to maintain high accuracy standards above 80%. Collaborated with machine learning engineers to train and evaluate YOLO-based models, identify labeling errors, and improve dataset quality to enhance model performance.

Annotated and curated large-scale image and video datasets for object detection and tracking models used in real-world computer vision applications. Responsibilities included drawing accurate bounding boxes, polygon segmentation, and object tracking across video frames for multiple object classes. Managed dataset preprocessing, augmentation, and version control using Roboflow while ensuring annotation consistency through detailed guideline adherence. Performed quality assurance reviews and inter-annotator agreement checks to maintain high accuracy standards above 80%. Collaborated with machine learning engineers to train and evaluate YOLO-based models, identify labeling errors, and improve dataset quality to enhance model performance.

2024

Education

Work History