Peter Muia

Key Skills

Software

Clickworker

CloudFactory

CVAT

Label Studio

OneForma

Remotasks

Scale AI

Telus

Appen

Top Subject Matter

No subject matter listed

Top Data Types

Image

Text

Video

Top Label Types

Audio Recording

Data Collection

Evaluation Rating

Prompt Response Writing SFT

Text Generation

Freelancer Overview

I am an experienced Data Annotation Specialist with a strong background in image verification, labeling, and AI training data. I have worked on large-scale annotation projects involving computer vision, text generation, and model evaluation, maintaining an average accuracy rate of 98%. My expertise spans data collection, bounding box labeling, and evaluation of AI-generated responses for relevance and safety. With a proven ability to follow structured guidelines, flag inconsistencies, and uphold high-quality standards, I take pride in producing precise and reliable datasets that improve machine learning models. I am also skilled in academic and content writing, which enhances my capacity to handle diverse annotation tasks requiring linguistic sensitivity and contextual understanding.

Labeling Experience

Image Verification and Annotation for AI Model Training

AppenImageEvaluation Rating

Contributed to high-quality AI training datasets by verifying, labeling, and classifying image and text data for computer vision and language model projects. Achieved 98% accuracy through consistent application of annotation standards and attention to detail. Collaborated with QA teams to review and validate annotations, improving model precision and reliability. Tasks included bounding box labeling, content classification, and prompt-response evaluation for AI-generated outputs.

Contributed to high-quality AI training datasets by verifying, labeling, and classifying image and text data for computer vision and language model projects. Achieved 98% accuracy through consistent application of annotation standards and attention to detail. Collaborated with QA teams to review and validate annotations, improving model precision and reliability. Tasks included bounding box labeling, content classification, and prompt-response evaluation for AI-generated outputs.

2025

Audio Data Collection and Transcription for Speech Recognition Models

TelusAudioEvaluation RatingData Collection

Collected and transcribed diverse audio datasets to support the development of speech recognition and natural language processing systems. Ensured data accuracy, clarity, and compliance with linguistic standards across multiple dialects. Evaluated and annotated audio clips for pronunciation, tone, and clarity. Collaborated with team members to improve labeling consistency and contribute to AI model quality assurance.

Collected and transcribed diverse audio datasets to support the development of speech recognition and natural language processing systems. Ensured data accuracy, clarity, and compliance with linguistic standards across multiple dialects. Evaluated and annotated audio clips for pronunciation, tone, and clarity. Collaborated with team members to improve labeling consistency and contribute to AI model quality assurance.

2025 - 2025

Education

Work History