Guerra Ramiro

Key Skills

Software

Labelbox

Top Subject Matter

AI/LLM evaluation in science and engineering domains

LLM factual reasoning in physics and mathematics

Top Data Types

Text

Top Task Types

No task types listed

Freelancer Overview

Freelance AI Research Evaluator & Data Consultant. Brings 7+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Labelbox, Snorkel Flow, and AnnotatePro. Education includes Master of Science, Florida State University (2024) and Bachelor of Science, Florida Atlantic University (2022). AI-training focus includes data types such as Text and labeling workflows including Evaluation and Rating.

Labeling Experience

Freelance AI Research Evaluator & Data Consultant

LabelboxText

Performed factual verification and quality assurance of AI-generated technical, mathematical, and scientific content for dataset integrity. Created and evaluated original problem sets to assess model reasoning and conceptual depth. Developed and applied evaluation guidelines focused on accuracy, factual robustness, and ethical bias detection. • Utilized platforms including Labelbox, Snorkel Flow, and AnnotatePro for annotation and review. • Evaluated and scored LLM outputs on technical correctness and contextual coherence. • Collaborated on creation of research-oriented benchmark datasets. • Applied bias-correction workflows to improve data fairness and reliability.

Performed factual verification and quality assurance of AI-generated technical, mathematical, and scientific content for dataset integrity. Created and evaluated original problem sets to assess model reasoning and conceptual depth. Developed and applied evaluation guidelines focused on accuracy, factual robustness, and ethical bias detection. • Utilized platforms including Labelbox, Snorkel Flow, and AnnotatePro for annotation and review. • Evaluated and scored LLM outputs on technical correctness and contextual coherence. • Collaborated on creation of research-oriented benchmark datasets. • Applied bias-correction workflows to improve data fairness and reliability.

2023 - Present

Factual Benchmarking in AI Reasoning Tasks (Research Project)

LabelboxText

Created factual benchmarking evaluation datasets for scientific reasoning tasks in physics and mathematics for LLMs. Designed and implemented rubrics to systematically measure LLM factual consistency and reasoning quality. Evaluated and annotated AI-generated scientific outputs for accuracy and contextual relevance. • Employed Python and Labelbox to curate and manage benchmark datasets. • Oversaw rubric-based rating of model responses for reasoning depth and factual integrity. • Assisted interdisciplinary teams in defining annotation protocols for technical content. • Documented quality assurance procedures for external research presentations and publications.

Created factual benchmarking evaluation datasets for scientific reasoning tasks in physics and mathematics for LLMs. Designed and implemented rubrics to systematically measure LLM factual consistency and reasoning quality. Evaluated and annotated AI-generated scientific outputs for accuracy and contextual relevance. • Employed Python and Labelbox to curate and manage benchmark datasets. • Oversaw rubric-based rating of model responses for reasoning depth and factual integrity. • Assisted interdisciplinary teams in defining annotation protocols for technical content. • Documented quality assurance procedures for external research presentations and publications.

2023 - 2023

Education

Work History