Joseph Wanzala

Key Skills

Software

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Top Task Types

Question Answering

Freelancer Overview

I am an experienced multimedia content reviewer and AI data training specialist with over five years dedicated to data annotation, quality assurance, and content evaluation. My background spans large-scale projects in computer vision, audio, and video data labeling, where I have partnered with leading AI research teams and contributed to initiatives with Google and Stanford HAI. I excel at tagging and evaluating complex datasets, ensuring high annotation accuracy and consistent adherence to guidelines. With a strong foundation in machine learning fundamentals and a keen analytical approach, I am committed to delivering precise, high-quality training data that drives improvements in multimodal AI systems. My ability to work independently and provide detailed feedback has helped optimize model performance and enhance data-driven solutions.

Labeling Experience

Data labeling

Scale AIComputer Code ProgrammingQuestion Answering

I worked on a large-scale LLM training project where I performed tasks such as text classification, instruction evaluation, response correction, and high-quality example generation. The project involved thousands of data points, and my role was to review, label, and refine model outputs to improve accuracy and alignment. Throughout the project, I followed strict quality guidelines, including task-specific rubrics, consistency checks, and safety standards, ensuring all annotations met the required accuracy and reliability levels.

I worked on a large-scale LLM training project where I performed tasks such as text classification, instruction evaluation, response correction, and high-quality example generation. The project involved thousands of data points, and my role was to review, label, and refine model outputs to improve accuracy and alignment. Throughout the project, I followed strict quality guidelines, including task-specific rubrics, consistency checks, and safety standards, ensuring all annotations met the required accuracy and reliability levels.

2020 - 2024

Education

Work History