Abe Luis Hinola

Key Skills

Software

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Text

Top Label Types

RLHF

Evaluation Rating

Red Teaming

Prompt Response Writing SFT

Audio Recording

Freelancer Overview

I am an experienced AI Trainer and Reviewer with a strong background in training large language models (LLMs) and ensuring high-quality AI outputs across multiple projects. My work at Outlier AI has allowed me to develop a keen eye for detail and accuracy in data labeling and annotation, meeting strict metrics and expectations while mentoring others as a senior reviewer. With over 10 years of professional experience, including leadership roles in medical billing, customer service, and training, I bring advanced English communication skills, adaptability, and a problem-solving mindset to every project. My expertise spans domains such as healthcare, customer service, and education, making me well-equipped to handle diverse data types and complex annotation tasks in AI and machine learning environments.

Labeling Experience

AI Trainer and Reviewer

Scale AITextRLHFEvaluation Rating

Worked on AI model evaluation and training projects at Outlier, focusing on prompt analysis, rubric-based assessment, and identification of planned failure modes to improve model reliability and response quality. Applied structured guidelines to rewrite prompts, create MECE evaluation criteria, and verify outputs for accuracy, clarity, and safety across diverse use cases. Contributed to iterative model improvement by delivering detailed feedback, edge-case analysis, and self-verifiable evaluations aligned with real-world user needs.

Worked on AI model evaluation and training projects at Outlier, focusing on prompt analysis, rubric-based assessment, and identification of planned failure modes to improve model reliability and response quality. Applied structured guidelines to rewrite prompts, create MECE evaluation criteria, and verify outputs for accuracy, clarity, and safety across diverse use cases. Contributed to iterative model improvement by delivering detailed feedback, edge-case analysis, and self-verifiable evaluations aligned with real-world user needs.

2024

Education

Work History