Peter Kuria

Key Skills

Software

Appen

Clickworker

CVAT

Data Annotation Tech

Labelbox

Label Studio

OneForma

Prodigy

Remotasks

Scale AI

Toloka

Other

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Image

Video

Top Task Types

Audio Recording

Bounding Box

Mapping

Object Detection

Segmentation

Freelancer Overview

I am an experienced AI training specialist with over five years of expertise in data annotation, evaluation, and labeling. My skills span across diverse domains, including image, video, audio, and text annotation, enabling me to contribute to the development of cutting-edge AI systems. I have worked on projects involving object detection, segmentation, classification, sentiment analysis, speech recognition, and text labeling for large language models (LLMs). My commitment to delivering high-quality datasets has consistently supported the development of accurate and efficient machine learning models across industries such as autonomous driving, e-commerce, healthcare, and natural language processing. In my roles with leading AI platforms such as Remotask, Appen, and Clickworker, I have developed a deep understanding of annotation tools like Labelbox, Scale AI, SuperAnnotate, and VoTT.

Labeling Experience

Freelance

Scale AIVideoBounding BoxSegmentation

The Autonomous Vehicle Project with Scale AI involved the development of large-scale, high-quality datasets to support machine learning models powering self-driving cars. The project encompassed a wide range of data annotation tasks aimed at enhancing the accuracy and reliability of autonomous systems in diverse environments, including urban streets, highways, and suburban areas. Key responsibilities included object detection and classification, where vehicles, pedestrians, traffic signs, and other roadway elements were identified and labeled. Semantic segmentation and pixel-level annotations were used to define lane markings, sidewalks, and road signs, while bounding box annotations provided spatial positioning for critical objects. Advanced techniques such as polyline annotation were applied to map lane boundaries and pathways, and 3D point cloud annotation captured spatial data from LiDAR scans to ensure depth and accuracy in object representation. Additionally, trajectory tracking

The Autonomous Vehicle Project with Scale AI involved the development of large-scale, high-quality datasets to support machine learning models powering self-driving cars. The project encompassed a wide range of data annotation tasks aimed at enhancing the accuracy and reliability of autonomous systems in diverse environments, including urban streets, highways, and suburban areas. Key responsibilities included object detection and classification, where vehicles, pedestrians, traffic signs, and other roadway elements were identified and labeled. Semantic segmentation and pixel-level annotations were used to define lane markings, sidewalks, and road signs, while bounding box annotations provided spatial positioning for critical objects. Advanced techniques such as polyline annotation were applied to map lane boundaries and pathways, and 3D point cloud annotation captured spatial data from LiDAR scans to ensure depth and accuracy in object representation. Additionally, trajectory tracking

2020 - 2023

Freelance

Scale AIDocumentClassificationText Generation

The project focused on categorizing and labeling text documents to train AI models for natural language processing applications such as document retrieval, topic classification, and sentiment analysis. Using structured guidelines, tasks involved assigning predefined categories to documents based on their content, identifying key topics, and annotating sentiment where applicable. Quality assurance was ensured by adhering to strict annotation guidelines, cross-validating labeled data, and maintaining consistency across all entries. Advanced annotation tools were utilized to streamline the categorization process and ensure high accuracy. This project delivered precise, well-organized datasets to support the development of efficient and reliable AI models for text-based applications.

The project focused on categorizing and labeling text documents to train AI models for natural language processing applications such as document retrieval, topic classification, and sentiment analysis. Using structured guidelines, tasks involved assigning predefined categories to documents based on their content, identifying key topics, and annotating sentiment where applicable. Quality assurance was ensured by adhering to strict annotation guidelines, cross-validating labeled data, and maintaining consistency across all entries. Advanced annotation tools were utilized to streamline the categorization process and ensure high accuracy. This project delivered precise, well-organized datasets to support the development of efficient and reliable AI models for text-based applications.

2022 - 2022

Freelance

OtherAudioAudio Recording

The project involved creating high-quality voice datasets using Pro Tools and Logic Pro to train AI systems like speech recognition and voice assistants. Tasks included recording diverse voices, transcribing audio, tagging speakers, emotions, and noise types, and labeling linguistic attributes like language and dialect. Quality was ensured through professional recording tools, accurate transcription, and adherence to ethical guidelines. AI-assisted tools streamlined processes like noise reduction and editing, resulting in precise and reliable datasets for advanced AI applications.

The project involved creating high-quality voice datasets using Pro Tools and Logic Pro to train AI systems like speech recognition and voice assistants. Tasks included recording diverse voices, transcribing audio, tagging speakers, emotions, and noise types, and labeling linguistic attributes like language and dialect. Quality was ensured through professional recording tools, accurate transcription, and adherence to ethical guidelines. AI-assisted tools streamlined processes like noise reduction and editing, resulting in precise and reliable datasets for advanced AI applications.

2018 - 2018

Education

Work History