Phillip Ronquillo

Key Skills

Software

Appen

Clickworker

CloudFactory

CrowdSource

Data Annotation Tech

Encord

Labelbox

Mindrift

Remotasks

Top Subject Matter

No subject matter listed

Top Data Types

3D Sensor

Audio

Computer Code Programming

Geospatial Tiled Imagery

Video

Top Label Types

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Entity Ner Classification

Segmentation

Freelancer Overview

I bring over 6 years of combined experience in chemistry, data science, and AI evaluation, with a strong focus on creating and refining high-quality training data for AI systems. My background includes designing chemistry-specific prompts, defining evaluation rubrics, and meticulously reviewing AI-generated outputs to ensure scientific accuracy and domain alignment. I am skilled in prompt engineering, model evaluation, and response correction, and have hands-on experience collaborating on generative AI projects within the life sciences. My technical toolkit includes Python, R, SQL, MATLAB, and Power BI, and I excel at translating complex scientific concepts into structured datasets for AI training and evaluation. Passionate about advancing AI applications in science, I thrive in remote, interdisciplinary teams and am committed to delivering precise, reliable data annotation and labeling for cutting-edge AI solutions.

Labeling Experience

Senior Annotator/Reviewer

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This project utilizes CloudFactory's managed workforce and Accelerated Annotation platform to deliver high quality, structured datasets for training machine learning models. By combining human expertise with AI powered automation including active learning and adaptive assistance the workflow achieves significantly faster labeling speeds while maintaining pixel level accuracy. Our approach ensures the creation of reliable ground truth data through a rigorous quality assurance process involving consensus scoring and expert oversight to minimize errors and handle complex edge cases.

This project utilizes CloudFactory's managed workforce and Accelerated Annotation platform to deliver high quality, structured datasets for training machine learning models. By combining human expertise with AI powered automation including active learning and adaptive assistance the workflow achieves significantly faster labeling speeds while maintaining pixel level accuracy. Our approach ensures the creation of reliable ground truth data through a rigorous quality assurance process involving consensus scoring and expert oversight to minimize errors and handle complex edge cases.

2022

Education

Work History