Dennis Kipngetich

Key Skills

Software

Appen

Argilla

Clickworker

CloudFactory

CVAT

Dataloop

Datasaur

Dataturk

Diffgram

Figure Eight

Hasty

HiveMind

Labelbox

LightTag

Lionbridge

Mighty AI

Playment

Remotasks

Scale AI

SuperAnnotate

Supervisely

Tagtog

Toloka

V7 Labs

Internal/Proprietary Tooling

Top Subject Matter

Self-driving Car Polyline

Satellite Image Classification

Text Entity Extracation

Self-driving Cars - Autonomous Driving

Medical Annotation - HealthCare

Text Entity Extraction

Prompt + Response Writing

Top Data Types

Document

Image

Text

Top Task Types

Classification

Entity (NER) Classification

Polygon

Prompt + Response Writing (SFT)

Segmentation

Freelancer Overview

I am a highly motivated and detail-oriented individual with extensive experience in data annotation, machine learning, and data analysis. My strong background in sociology, criminology, and community development has fueled my passion for using data to drive positive social change. Over the years, I have worked in various data labeling and annotation roles in companies such as Focal Systems, RetinAI, Gatik, and CloudFactory, where I have successfully annotated various datasets for training AI models.

Labeling Experience

LLM Data Trainer

SuperannotateTextText GenerationEvaluation Rating

I utilize the SuperAnnotate tool to generate promotes and responses to be used in training an AI model that provides helpful, honest, and harmless responses to users while maintaining respectfulness and diversity in its interactions. • Trained AI Model by generating and utilizing accurate information from reliable sources to ensure helpful, honest, and harmless responses to user queries. • Ensured responses aligned with the 3-H principles - Helpfulness, Honesty, and Harmlessness - by prioritizing accurate information, maintaining a consistent tone, and providing diverse and rich information when necessary. • Upheld content safety standards by avoiding personally identifiable information, ensuring all data was human-written, and refraining from using LLM-generated content to maintain quality and integrity in responses. • Enforced writing guidelines, including proper grammar, spelling, and adherence to Standard American English, while also focusing on content length, richness, and variet

I utilize the SuperAnnotate tool to generate promotes and responses to be used in training an AI model that provides helpful, honest, and harmless responses to users while maintaining respectfulness and diversity in its interactions. • Trained AI Model by generating and utilizing accurate information from reliable sources to ensure helpful, honest, and harmless responses to user queries. • Ensured responses aligned with the 3-H principles - Helpfulness, Honesty, and Harmlessness - by prioritizing accurate information, maintaining a consistent tone, and providing diverse and rich information when necessary. • Upheld content safety standards by avoiding personally identifiable information, ensuring all data was human-written, and refraining from using LLM-generated content to maintain quality and integrity in responses. • Enforced writing guidelines, including proper grammar, spelling, and adherence to Standard American English, while also focusing on content length, richness, and variet

2023 - 2023

AI Data Specialist

Internal Proprietary ToolingDocumentPolygonPolyline

I contributed to large-scale data labeling and content annotation projects aimed at enhancing the platform’s discovery and personalization systems. My role involved creating structured classification frameworks, annotating multimodal datasets (text, audio, and visual), and integrating automation to scale labeling workflows. I conducted A/B testing and evaluation experiments to assess content quality, engagement, and accuracy, achieving over 98% consistency across labeling outputs. The project scope covered millions of data points, requiring robust quality assurance pipelines, cross-functional collaboration with product and engineering teams, and the development of reusable evaluation templates to ensure efficiency and high standards across multiple annotation tasks

I contributed to large-scale data labeling and content annotation projects aimed at enhancing the platform’s discovery and personalization systems. My role involved creating structured classification frameworks, annotating multimodal datasets (text, audio, and visual), and integrating automation to scale labeling workflows. I conducted A/B testing and evaluation experiments to assess content quality, engagement, and accuracy, achieving over 98% consistency across labeling outputs. The project scope covered millions of data points, requiring robust quality assurance pipelines, cross-functional collaboration with product and engineering teams, and the development of reusable evaluation templates to ensure efficiency and high standards across multiple annotation tasks

2024

Data Labeler - Internal Reviewer

Internal Proprietary ToolingImageBounding BoxClassification

• Quality Control: I have extensive experience ensuring quality control through intermittent reviews, adhering to established standards and guidelines. My attention to detail and commitment to accuracy guarantee the delivery of high-quality work that meets client expectations. • Data Reviewing: With a track record of completing data reviewing tasks up to 98% accuracy, I excel in working within tight deadlines. I have successfully reviewed over 1,000,000 images over a span of 2+ years, resulting in an overall reduction of manual task labelling time. My skills in data reviewing enable me to deliver quality work that meets client expectations consistently. • Task Management: I possess expertise in utilizing internal tools and software for task assignment and tracking. Additionally, I have managed programs for labelling, annotation, and validation, ensuring the delivery of highquality work within stipulated timelines.

• Quality Control: I have extensive experience ensuring quality control through intermittent reviews, adhering to established standards and guidelines. My attention to detail and commitment to accuracy guarantee the delivery of high-quality work that meets client expectations. • Data Reviewing: With a track record of completing data reviewing tasks up to 98% accuracy, I excel in working within tight deadlines. I have successfully reviewed over 1,000,000 images over a span of 2+ years, resulting in an overall reduction of manual task labelling time. My skills in data reviewing enable me to deliver quality work that meets client expectations consistently. • Task Management: I possess expertise in utilizing internal tools and software for task assignment and tracking. Additionally, I have managed programs for labelling, annotation, and validation, ensuring the delivery of highquality work within stipulated timelines.

2021 - 2023

Medical Data Annotator

Internal Proprietary ToolingMedical DicomPolylineSegmentation

• Image Annotation: With my expertise in annotation and having annotated retinal disease images using cutting-edge software, RetinAI Discovery, I streamlined the annotation process and reduced the likelihood of errors, resulting in more accurate diagnoses and improved patient outcomes. • Image Analysis Automation: With a deep understanding of various imaging modalities and techniques, I successfully automated the annotation of retinal diseases, RetinAI Discovery. This process hasreduced the workload on clinicians, and improved the efficiency and accuracy of diagnostic procedures • Rapid Identification: In Ophthalmology, quick and accurate diagnosis and treatment decisions are crucial. I did enable this through the rapid identification and annotation of retinal diseases. By delivering high-quality results within tight timelines, I contributed to improved patient outcomes and overall healthcare efficiency by the Academic Researchers

• Image Annotation: With my expertise in annotation and having annotated retinal disease images using cutting-edge software, RetinAI Discovery, I streamlined the annotation process and reduced the likelihood of errors, resulting in more accurate diagnoses and improved patient outcomes. • Image Analysis Automation: With a deep understanding of various imaging modalities and techniques, I successfully automated the annotation of retinal diseases, RetinAI Discovery. This process hasreduced the workload on clinicians, and improved the efficiency and accuracy of diagnostic procedures • Rapid Identification: In Ophthalmology, quick and accurate diagnosis and treatment decisions are crucial. I did enable this through the rapid identification and annotation of retinal diseases. By delivering high-quality results within tight timelines, I contributed to improved patient outcomes and overall healthcare efficiency by the Academic Researchers

2022 - 2022

Data Labeler

CVATImageBounding BoxPolygon

• Leveraged expertise in data labeling and annotation to accurately classify over 20,000 images for use in a machine learning model, resulting in a 98% accuracy rate. • Collaborated with cross-functional team members to identify areas of improvement in data labeling processes, leading to a 30% increase in efficiency and reducing project turnaround time by two weeks. • Participated in onboarding and comprehensive training and guidance to new team members on effective communication approaches when working through complex problems during the labeling process, resulting in a 70% reduction in errors and ensuring timely completion of projects.

• Leveraged expertise in data labeling and annotation to accurately classify over 20,000 images for use in a machine learning model, resulting in a 98% accuracy rate. • Collaborated with cross-functional team members to identify areas of improvement in data labeling processes, leading to a 30% increase in efficiency and reducing project turnaround time by two weeks. • Participated in onboarding and comprehensive training and guidance to new team members on effective communication approaches when working through complex problems during the labeling process, resulting in a 70% reduction in errors and ensuring timely completion of projects.

2020 - 2021

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Education

Work History