Dominic Mburu

Key Skills

Software

Clickworker

Data Annotation Tech

Mindrift

Remotasks

Toloka

Scale AI

CVAT

Top Subject Matter

Computer Vision

Speech Recognition

Natural Language Processing (NLP)

Top Data Types

Document

Image

Text

Top Label Types

Data Collection

Evaluation Rating

Prompt Response Writing SFT

Question Answering

Text Generation

Freelancer Overview

I am an experienced data annotation specialist with over 2 years of expertise in creating high-quality training data for machine learning and AI applications. My background spans a diverse range of annotation tasks including text classification, named entity recognition, image object detection, and video labeling for computer vision projects. I have completed complex annotation projects for leading crowdsourcing platforms like Mindrift, Remotask, Toloka, and Clickworker, consistently maintaining accuracy rates above 98%. My key strengths include meticulous attention to detail, the ability to quickly adapt to new annotation guidelines and tools, and a deep understanding of how high-quality labeled data impacts AI model performance. I have specialized experience in sentiment analysis, intent classification for chatbots, and video annotation for autonomous vehicle datasets. My combination of technical skills in data annotation along with my analytical background in business and accounting allows me to approach labeling tasks with both precision and business context awareness. I am passionate about contributing to cutting-edge AI advancements through my annotation work.

Labeling Experience

Video Annotation for Autonomous Vehicle Dataset

CVATVideoBounding BoxPolygon

I contributed to a large-scale video annotation project for training autonomous vehicle AI systems. The project involved labeling and tracking various objects in street-view video footage captured from multiple camera angles on test vehicles. Key responsibilities included: Creating precise bounding boxes around vehicles, pedestrians, cyclists, and traffic signs Using polygon tools to segment road markings, sidewalks, and other irregular shapes Applying polylines to delineate lane boundaries and road edges Tracking objects across multiple frames to ensure consistent labeling Annotating weather conditions and time of day for each video segment Identifying and labeling traffic lights, including their current state (red, yellow, green) I maintained a 98% accuracy rate while processing over 1000 hours of video footage. My attention to detail was crucial in handling challenging scenarios such as partially obscured objects, varying lighting conditions, and complex urban environments.

I contributed to a large-scale video annotation project for training autonomous vehicle AI systems. The project involved labeling and tracking various objects in street-view video footage captured from multiple camera angles on test vehicles. Key responsibilities included: Creating precise bounding boxes around vehicles, pedestrians, cyclists, and traffic signs Using polygon tools to segment road markings, sidewalks, and other irregular shapes Applying polylines to delineate lane boundaries and road edges Tracking objects across multiple frames to ensure consistent labeling Annotating weather conditions and time of day for each video segment Identifying and labeling traffic lights, including their current state (red, yellow, green) I maintained a 98% accuracy rate while processing over 1000 hours of video footage. My attention to detail was crucial in handling challenging scenarios such as partially obscured objects, varying lighting conditions, and complex urban environments.

2023 - 2024

Education

Work History