Jeremy Paul

Key Skills

Software

Labelbox

Top Subject Matter

No subject matter listed

Top Data Types

Image

Top Label Types

Classification

Question Answering

Object Detection

Evaluation Rating

Freelancer Overview

I am an AI Data Annotation and Quality Analyst with 3 years of hands-on experience specializing in AI evaluation, data labeling, and quality assurance for Q&A-style AI systems. My expertise includes evaluating AI-generated responses for factual accuracy, reasoning depth, and adherence to guidelines, with a strong focus on reducing hallucinations and ensuring technical correctness in domains such as software engineering, Kotlin/JVM concepts, Android testing, and CI/CD workflows. I am skilled in rubric-based evaluation, model alignment, and maintaining high throughput while ensuring consistency and compliance with safety and policy standards. My technical toolkit includes Python, SQL, Git, and tools like TestRail, as well as experience with automated and manual testing using JUnit 5, MockK, Espresso, and Robolectric. I am passionate about delivering high-quality training data that drives better AI performance.

Labeling Experience

*AI Data Annotator - Image-Based Data Labeling & Annotation Specialist

LabelboxImageClassificationQuestion Answering

Project Description Currently contributing to multimodal AI improvement through image-based Q&A annotation and evaluation. Project involves analyzing AI-generated responses to image inputs across technical and software engineering domains. Scope & Tasks: Evaluate AI responses to image queries for factual accuracy and visual reasoning using Labelbox Perform hallucination detection by verifying AI claims against image content Provide written justifications for ratings to support model fine-tuning Assess visual reasoning and edge cases (blurry images, diagrams, UI screenshots) Label image content including: UI mockups, Android screenshots, code snippets, and engineering diagrams Project Size: High-volume environment processing hundreds of image evaluations weekly Quality Measures: Rubric-based evaluation, policy compliance, and regular calibration sessions

Project Description Currently contributing to multimodal AI improvement through image-based Q&A annotation and evaluation. Project involves analyzing AI-generated responses to image inputs across technical and software engineering domains. Scope & Tasks: Evaluate AI responses to image queries for factual accuracy and visual reasoning using Labelbox Perform hallucination detection by verifying AI claims against image content Provide written justifications for ratings to support model fine-tuning Assess visual reasoning and edge cases (blurry images, diagrams, UI screenshots) Label image content including: UI mockups, Android screenshots, code snippets, and engineering diagrams Project Size: High-volume environment processing hundreds of image evaluations weekly Quality Measures: Rubric-based evaluation, policy compliance, and regular calibration sessions

2024 - 2025

Education

Work History