Ryan Mokua

Key Skills

Software

Labelbox

Appen

CVAT

Top Subject Matter

No subject matter listed

Top Data Types

Image

Audio

Text

Top Label Types

Polygon

Classification

Bounding Box

Entity Ner Classification

Freelancer Overview

I am a detail-oriented AI training specialist with hands-on experience in data annotation, prompt evaluation, content moderation, and quality assurance for machine learning projects. My background spans NLP and computer vision domains, where I have labeled and structured large datasets, performed intent classification, entity recognition, sentiment analysis, and object detection annotation. I am skilled in using tools like Labelbox, CVAT, Scale AI, Appen, and Dataloop, and have maintained a 98–99% quality rating across multiple remote AI training cycles. My work includes evaluating AI-generated responses, cleaning and validating datasets, and ensuring strict compliance with evolving annotation guidelines. With strong analytical skills, a keen eye for detail, and a commitment to data quality, I am passionate about supporting the development of high-performing AI systems.

Labeling Experience

Senior Data Annotation Specialist

LabelboxImagePolygon

As Senior Data Annotation Specialist, I annotated over 80,000 images for object detection and segmentation tasks in computer vision datasets. I performed polygon segmentation for complex objects, frequently achieving a 99% QA approval rate. I also labeled natural language processing datasets for intent classification and entity recognition. • Interpreted and implemented evolving annotation guidelines. • Conducted peer review QA to improve team annotation accuracy from 93% to 98%. • Consistently met a daily target of 1,500+ high-precision annotations. • Maintained strict confidentiality and compliance standards.

As Senior Data Annotation Specialist, I annotated over 80,000 images for object detection and segmentation tasks in computer vision datasets. I performed polygon segmentation for complex objects, frequently achieving a 99% QA approval rate. I also labeled natural language processing datasets for intent classification and entity recognition. • Interpreted and implemented evolving annotation guidelines. • Conducted peer review QA to improve team annotation accuracy from 93% to 98%. • Consistently met a daily target of 1,500+ high-precision annotations. • Maintained strict confidentiality and compliance standards.

2024

AI Data Labeler (Contract)

AppenAudioClassification

As an AI Data Labeler (Contract), I transcribed and labeled over 200 hours of speech data for voice assistant model training. I categorized complex e-commerce datasets for supervised machine learning use. I proactively flagged and escalated edge cases and ambiguous data during labeling. • Cleaned and prepared raw datasets prior to training pipeline ingestion. • Maintained top 10% productivity among more than 50 annotators. • Focused on speech, text, and e-commerce dataset classification. • Conducted quality control throughout all labeling stages.

As an AI Data Labeler (Contract), I transcribed and labeled over 200 hours of speech data for voice assistant model training. I categorized complex e-commerce datasets for supervised machine learning use. I proactively flagged and escalated edge cases and ambiguous data during labeling. • Cleaned and prepared raw datasets prior to training pipeline ingestion. • Maintained top 10% productivity among more than 50 annotators. • Focused on speech, text, and e-commerce dataset classification. • Conducted quality control throughout all labeling stages.

2023 - 2024

Speech Recognition Data Transcriber

AppenAudioClassification

In a speech recognition dataset project, I performed timestamped audio transcription for language model training. I verified accent variations and background noise tagging according to project guidelines. My annotations supported the development of robust speech recognition technology. • Labeled audio segments for speaker and environment characteristics. • Maintained quality control across transcription tasks. • Collaborated with speech model engineers on dataset iterations. • Ensured adherence to detailed labeling criteria.

In a speech recognition dataset project, I performed timestamped audio transcription for language model training. I verified accent variations and background noise tagging according to project guidelines. My annotations supported the development of robust speech recognition technology. • Labeled audio segments for speaker and environment characteristics. • Maintained quality control across transcription tasks. • Collaborated with speech model engineers on dataset iterations. • Ensured adherence to detailed labeling criteria.

2023 - 2023

NLP Chatbot Training Data Annotator

AppenTextEntity Ner Classification

For an NLP customer support chatbot project, I labeled user intents and extracted named entities from over 25,000 chat conversation samples. My work improved chatbot response accuracy for customer support queries. I followed complex guidelines to ensure clarity and quality of labeled text data. • Enhanced chatbot performance via curated datasets. • Focused on intent classification and entity extraction. • Labeled high-volume chat and text samples. • Maintained high annotation accuracy standards.

For an NLP customer support chatbot project, I labeled user intents and extracted named entities from over 25,000 chat conversation samples. My work improved chatbot response accuracy for customer support queries. I followed complex guidelines to ensure clarity and quality of labeled text data. • Enhanced chatbot performance via curated datasets. • Focused on intent classification and entity extraction. • Labeled high-volume chat and text samples. • Maintained high annotation accuracy standards.

2023 - 2023

Computer Vision Project Data Annotator

CVATImageBounding Box

In a computer vision object detection project, I labeled traffic signs, pedestrians, and vehicles using bounding boxes and segmentation masks. I enhanced model accuracy by maintaining consistent annotation standards. My work contributed to a reduction in model false positives in autonomous driving datasets. • Ensured annotation consistency across thousands of image frames. • Used segmentation masks and bounding boxes for object identification. • Supported autonomous driving application development. • Adhered to evolving annotation specifications and guidelines.

In a computer vision object detection project, I labeled traffic signs, pedestrians, and vehicles using bounding boxes and segmentation masks. I enhanced model accuracy by maintaining consistent annotation standards. My work contributed to a reduction in model false positives in autonomous driving datasets. • Ensured annotation consistency across thousands of image frames. • Used segmentation masks and bounding boxes for object identification. • Supported autonomous driving application development. • Adhered to evolving annotation specifications and guidelines.

2023 - 2023

Education

Work History