Mark Kiogora

Key Skills

Software

CVAT

Labelbox

Scale AI

CloudFactory

Top Subject Matter

Autonomous vehicles

Surveillance Domain Expertise

pedestrian detection

Top Data Types

Video

Image

Text

Document

Top Task Types

Bounding Box

Segmentation

Point Key Point

Freelancer Overview

Senior Video Annotator. Core strengths include CVAT, Labelbox, and VGG Image Annotator. Education includes a Bachelor of Science from Jomo Kenyatta University of Agriculture and Technology (2027). AI-training focus includes data types such as Video and labeling workflows, including Bounding Box, Segmentation, and Point.

Labeling Experience

Senior Video Annotator

CVATVideoBounding Box

As a Senior Video Annotator at Apex Data Labs, I labeled video frames for autonomous driving and surveillance machine learning projects. I performed object tracking, bounding box, and polygon annotations while ensuring high-quality standards across millions of frames. My work involved project collaboration, QA audits, and resolving complex annotation cases. • Delivered over 3.2 million annotated frames across 18 enterprise projects. • Maintained a 99.1% accuracy rate while labeling 500–700 video frames per day. • Used CVAT and Labelbox for multi-class object tracking and annotation tasks. • Provided structured QA feedback to maintain team accuracy above 97%.

As a Senior Video Annotator at Apex Data Labs, I labeled video frames for autonomous driving and surveillance machine learning projects. I performed object tracking, bounding box, and polygon annotations while ensuring high-quality standards across millions of frames. My work involved project collaboration, QA audits, and resolving complex annotation cases. • Delivered over 3.2 million annotated frames across 18 enterprise projects. • Maintained a 99.1% accuracy rate while labeling 500–700 video frames per day. • Used CVAT and Labelbox for multi-class object tracking and annotation tasks. • Provided structured QA feedback to maintain team accuracy above 97%.

2022 - Present

Video Annotator

VideoSegmentation

As a Video Annotator at DataForce by TransPerfect, I focused on annotating datasets for Level 4 autonomous vehicle training. I specialized in semantic segmentation and object labeling for pedestrian, vehicle, and traffic analyses. My contributions included maintaining quality standards and assisting in training new annotators. • Labeled 1.5 million frames with a 98.4% quality score over 20 months. • Specialized in semantic segmentation using VGG Image Annotator and Scale AI. • Annotated multi-class objects for autonomous vehicle pipelines. • Assisted in onboarding and training 8 new annotators.

As a Video Annotator at DataForce by TransPerfect, I focused on annotating datasets for Level 4 autonomous vehicle training. I specialized in semantic segmentation and object labeling for pedestrian, vehicle, and traffic analyses. My contributions included maintaining quality standards and assisting in training new annotators. • Labeled 1.5 million frames with a 98.4% quality score over 20 months. • Specialized in semantic segmentation using VGG Image Annotator and Scale AI. • Annotated multi-class objects for autonomous vehicle pipelines. • Assisted in onboarding and training 8 new annotators.

2020 - 2021

Data Labeling Specialist (Contract)

CloudfactoryVideoPoint Key Point

As a Data Labeling Specialist at CloudFactory, I worked on sports footage annotation for player tracking and event detection models. My responsibilities included keypoint annotation for human pose estimation across various sports. I collaborated with QA teams to manage labeling ambiguities and ensure dataset consistency. • Labeled sports videos for soccer, basketball, and tennis analytics. • Completed keypoint annotation at a pace 25% above team average. • Regularly flagged ambiguous frames for quality review. • Worked with QA to resolve labeling inconsistencies.

As a Data Labeling Specialist at CloudFactory, I worked on sports footage annotation for player tracking and event detection models. My responsibilities included keypoint annotation for human pose estimation across various sports. I collaborated with QA teams to manage labeling ambiguities and ensure dataset consistency. • Labeled sports videos for soccer, basketball, and tennis analytics. • Completed keypoint annotation at a pace 25% above team average. • Regularly flagged ambiguous frames for quality review. • Worked with QA to resolve labeling inconsistencies.

2019 - 2020

Education

Work History