For employers

Hire this AI Trainer

Sign in or create an account to invite AI Trainers to your job.

Invite to Job
Samuel Nduati

Samuel Nduati

AI Trainer - Machine Learning & Generative AI

KENYA flag
Nairobi, Kenya
$10.00/hrIntermediateLabelbox

Key Skills

Software

LabelboxLabelbox

Top Subject Matter

No subject matter listed

Top Data Types

VideoVideo

Top Label Types

Question Answering
Emotion Recognition
Tracking

Freelancer Overview

I am a detail-oriented AI Trainer with over 5 years of experience specializing in data annotation, labeling, and training data support for machine learning and generative AI systems. My background spans large-scale projects in computer vision, natural language processing (NLP), and multimodal AI, where I have consistently delivered high-quality labeled datasets with 98%+ accuracy. I am skilled in tasks such as entity recognition, sentiment analysis, bounding box/object detection, semantic segmentation, and prompt-response evaluation. I have hands-on expertise with tools like LabelStudio, CVAT, Prodigy, Scale AI, and Amazon SageMaker Ground Truth, and I regularly collaborate with data scientists and engineers to improve model performance through iterative feedback. I am passionate about advancing responsible AI and have experience training and mentoring annotation teams, developing guidelines, and ensuring data quality for diverse, multilingual applications.

IntermediateEnglish

Labeling Experience

Labelbox

Senior AI trainer

LabelboxVideoQuestion AnsweringEmotion Recognition
The project I contributed to involved defining and executing high-precision data labeling efforts as a foundational phase to support clinically reliable diagnostic and predictive models, while ensuring strict adherence to regulatory standards (HIPAA, GDPR, FDA SaMD guidelines) and patient safety priorities; this included managing annotation tasks across key modalities such as bounding box, polygon, and pixel-level semantic segmentation on medical imaging (X-ray, CT, MRI, pathology slides) for tumor/lesion detection, organ segmentation, landmark placement, and severity grading; named entity recognition, relation extraction, assertion detection, and ICD-10/CPT coding in unstructured EHR/clinical text for phenotyping and risk stratification; temporal event detection, segmentation, and classification in physiological signals (ECG, EEG, PPG) for arrhythmia or seizure identification; phase boundary labeling, tool tracking, and action recognition in surgical video

The project I contributed to involved defining and executing high-precision data labeling efforts as a foundational phase to support clinically reliable diagnostic and predictive models, while ensuring strict adherence to regulatory standards (HIPAA, GDPR, FDA SaMD guidelines) and patient safety priorities; this included managing annotation tasks across key modalities such as bounding box, polygon, and pixel-level semantic segmentation on medical imaging (X-ray, CT, MRI, pathology slides) for tumor/lesion detection, organ segmentation, landmark placement, and severity grading; named entity recognition, relation extraction, assertion detection, and ICD-10/CPT coding in unstructured EHR/clinical text for phenotyping and risk stratification; temporal event detection, segmentation, and classification in physiological signals (ECG, EEG, PPG) for arrhythmia or seizure identification; phase boundary labeling, tool tracking, and action recognition in surgical video

2022 - 2025

Education

J

Jomo Kenyatta University of Agriculture and Technology

Bachelor of Science, Computer Science

Bachelor of Science
2014 - 2018

Work History

M

MUKESA Engineers

Senior AI Trainer

Nairobi
2022 - 2025