Henry Guda

Key Skills

Software

Labelbox

Appen

SuperAnnotate

Top Subject Matter

Computer Vision Datasets for Industrial AI

Quality Testing of Visual Data Labeling for AI

Generative AI Dataset Tagging and Curation

Top Data Types

Image

Text

Top Task Types

Segmentation

Classification

Freelancer Overview

Lead AI Trainer and Data Labeling Strategist. Core strengths include Labelbox, Appen, and SuperAnnotate. Education includes Doctor of Philosophy, Jomo Kenyatta University of Agriculture and Technology (2020) and Master of Science, Jomo Kenyatta University of Agriculture and Technology (2014). AI-training focus includes data types such as Image and labeling workflows including Segmentation and Classification.

Labeling Experience

Lead AI Trainer and Data Labeling Strategist

LabelboxImageSegmentation

Led end-to-end image dataset annotation pipelines for AI models utilized in healthcare, retail, and autonomous systems. Designed, implemented, and optimized scalable workflows ensuring consistency and precision above 98% over millions of annotated samples. Developed and introduced automated error-detection systems that significantly reduced discrepancies and improved model training outputs. • Oversaw human annotators and ensured communication with engineering teams • Established and monitored custom metrics for dataset validation • Implemented best practices for quality assurance and bias reduction • Facilitated the integration of automation into human annotation cycles

Led end-to-end image dataset annotation pipelines for AI models utilized in healthcare, retail, and autonomous systems. Designed, implemented, and optimized scalable workflows ensuring consistency and precision above 98% over millions of annotated samples. Developed and introduced automated error-detection systems that significantly reduced discrepancies and improved model training outputs. • Oversaw human annotators and ensured communication with engineering teams • Established and monitored custom metrics for dataset validation • Implemented best practices for quality assurance and bias reduction • Facilitated the integration of automation into human annotation cycles

2015 - Present

Project Coffee: AI Data Labeling Contributor

SuperannotateImageClassification

Participated in a multimodal dataset AI labeling project focused on short-form video tagging and image editing. Completed stringent calibration and qualification procedures with exemplary quality results. Provided human feedback and detailed visual dataset curation for generative AI model training. • Performed tasks involving tagging, analysis, and data qualification • Assisted in multimodal annotation with both images and short video • Enhanced annotation accuracy through continuous calibration • Contributed training samples with human-in-the-loop feedback

Participated in a multimodal dataset AI labeling project focused on short-form video tagging and image editing. Completed stringent calibration and qualification procedures with exemplary quality results. Provided human feedback and detailed visual dataset curation for generative AI model training. • Performed tasks involving tagging, analysis, and data qualification • Assisted in multimodal annotation with both images and short video • Enhanced annotation accuracy through continuous calibration • Contributed training samples with human-in-the-loop feedback

2025 - 2025

AI Labeling Pipeline Optimization Study (Independent Research)

ImageSegmentation

Independently developed and researched an annotation framework integrating probabilistic error modeling with human verification loops for improved precision. Demonstrated measurable advances in model performance through reduced variance in human-labeled image datasets. Utilized the established framework to refine visual data quality and annotation reliability. • Developed research-based annotation solutions for improved data integrity • Devised probabilistic modeling for smarter error detection • Integrated quality feedback for continuous annotation improvement • Contributed research to support AI image model development and optimization

Independently developed and researched an annotation framework integrating probabilistic error modeling with human verification loops for improved precision. Demonstrated measurable advances in model performance through reduced variance in human-labeled image datasets. Utilized the established framework to refine visual data quality and annotation reliability. • Developed research-based annotation solutions for improved data integrity • Devised probabilistic modeling for smarter error detection • Integrated quality feedback for continuous annotation improvement • Contributed research to support AI image model development and optimization

2023 - 2023

Senior Quality Evaluator and Testing Analyst

AppenImageClassification

Designed and administered data labeling proficiency tests for visual annotation contributors. Evaluated and calibrated the consistency and accuracy of annotators using structured test frameworks across multiple projects. Advanced onboarding standards and qualification assessments to streamline dataset pipeline readiness. • Contributed labeling evaluation protocols for benchmark establishment • Enhanced test framework for annotator skill validation • Improved contributor performance through targeted feedback • Supported data labeling oversight for large-scale production and research datasets

Designed and administered data labeling proficiency tests for visual annotation contributors. Evaluated and calibrated the consistency and accuracy of annotators using structured test frameworks across multiple projects. Advanced onboarding standards and qualification assessments to streamline dataset pipeline readiness. • Contributed labeling evaluation protocols for benchmark establishment • Enhanced test framework for annotator skill validation • Improved contributor performance through targeted feedback • Supported data labeling oversight for large-scale production and research datasets

2012 - 2015

Education

Work History