Mario Henríquez

Key Skills

Software

Labelbox

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Top Task Types

Computer Programming Coding

Data Collection

Prompt Response Writing SFT

Text Summarization

Freelancer Overview

Experienced Computer Engineer with a strong background in AI training data, specializing in task design, evaluation, and prompt engineering. Since early 2025, I have worked as a freelance AI coding expert, contributing to large-scale RLHF (Reinforcement Learning from Human Feedback) workflows across multiple projects. My focus includes reviewing code generation tasks, validating test logs, analyzing model outputs, and ensuring alignment with strict rubrics and quality standards. With a technical foundation in Python, JavaScript, SQL, and full-stack development, I bring both coding expertise and critical analysis to the AI training process. I have designed and evaluated complex multi-turn prompts, developed simulation-based tasks in Python and JS, and conducted peer reviews as a trusted reviewer. My experience balancing linguistic clarity, intent coverage, and rubric compliance enables me to consistently deliver high-quality data for training state-of-the-art language models.

Labeling Experience

Outlier AI

Scale AIComputer Code ProgrammingRLHFEvaluation Rating

I worked on a large-scale AI training data project involving the evaluation and refinement of LLM-generated outputs for code generation, multi-turn prompts, and reasoning tasks. The scope included creating high-quality training data through rigorous rubric-based review, validation of test logs, and refinement of prompts for intent clarity and instructional alignment. Tasks included reviewing code diffs, validating unit test coverage based on execution logs, rewriting problem statements and requirements to match original inputs, and scoring outputs using structured evaluation criteria. The project operated at scale, with hundreds of daily annotations and peer-reviewed submissions. Quality standards were strict, including log-based test validation, rubric adherence, and multi-stage review workflows to ensure consistency, fairness, and reproducibility.

I worked on a large-scale AI training data project involving the evaluation and refinement of LLM-generated outputs for code generation, multi-turn prompts, and reasoning tasks. The scope included creating high-quality training data through rigorous rubric-based review, validation of test logs, and refinement of prompts for intent clarity and instructional alignment. Tasks included reviewing code diffs, validating unit test coverage based on execution logs, rewriting problem statements and requirements to match original inputs, and scoring outputs using structured evaluation criteria. The project operated at scale, with hundreds of daily annotations and peer-reviewed submissions. Quality standards were strict, including log-based test validation, rubric adherence, and multi-stage review workflows to ensure consistency, fairness, and reproducibility.

2024

Education

Work History