Guillermo Villar Sanchez

Key Skills

Software

Scale AI

Surge AI

Telus

Internal/Proprietary Tooling

Data Annotation Tech

Remotasks

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Computer Code Programming

Text

Top Task Types

Audio Recording

Classification

Computer Programming Coding

Evaluation Rating

Prompt Response Writing SFT

Freelancer Overview

As a freelance AI contributor with Outlier and DataAnnotation, I’ve worked on projects focused on high-quality data labeling, translation, and prompt engineering for large-scale language models. My work involves translating between Spanish and English, evaluating and rewriting text completions, and designing language-specific prompts to improve model accuracy and nuance. I consistently deliver precise annotations and linguistic insights that help refine model behavior across multilingual contexts. In addition to linguistic work, I bring technical expertise in Python, JavaScript, and other programming languages, allowing me to contribute to code-based evaluation tasks and logic understanding for coding-capable LLMs. My dual skill set—both technical and linguistic—positions me well for roles that demand a deep understanding of training data quality, cross-language alignment, and thoughtful evaluation strategies in AI development.

Labeling Experience

Image and Audio prompt code generation

Scale AIAudioEvaluation Rating

Created and annotated prompts combining code snippets, charts, and diagrams to assess LLM capabilities in tasks such as bug fixing, algorithmic reasoning, and logic explanation — often framed entirely in Spanish. Evaluated LLM output for accuracy, clarity, and alignment with intended logic across multiple programming languages, including Python, JavaScript, and pseudocode-like instruction. Translated complex technical tasks into Spanish while preserving pedagogical intent, enabling broader accessibility and model coverage across non-English code learners. Maintained strict accuracy standards, contributing to internal datasets used for model fine-tuning and benchmark evaluation by AI research teams. Supported iterative refinement of prompt templates and evaluation rubrics, helping shape what "good" outputs should look like across multilingual and multimodal tasks.

Created and annotated prompts combining code snippets, charts, and diagrams to assess LLM capabilities in tasks such as bug fixing, algorithmic reasoning, and logic explanation — often framed entirely in Spanish. Evaluated LLM output for accuracy, clarity, and alignment with intended logic across multiple programming languages, including Python, JavaScript, and pseudocode-like instruction. Translated complex technical tasks into Spanish while preserving pedagogical intent, enabling broader accessibility and model coverage across non-English code learners. Maintained strict accuracy standards, contributing to internal datasets used for model fine-tuning and benchmark evaluation by AI research teams. Supported iterative refinement of prompt templates and evaluation rubrics, helping shape what "good" outputs should look like across multilingual and multimodal tasks.

2024

Coding LLM error finding and annotation

Data Annotation TechComputer Code ProgrammingText Generation

AI Data Labeling Specialist — DataAnnotation (Freelance) Remote · 2024 – Present Contributed to a high-impact multilingual project for a leading AI research lab, focused on improving the reasoning, code generation, and translation capabilities of large language models (LLMs). As a bilingual engineer, I was selected for tasks requiring both Spanish–English fluency and programming proficiency to support the development of robust, globally deployable AI systems. Translated and annotated complex Spanish–English prompts and completions, ensuring cultural, grammatical, and contextual accuracy across diverse use cases (e.g., search queries, dialogue, summarization). Applied programming knowledge (Python, JavaScript, C++) to evaluate code completions, debug logic errors, and design prompts that test reasoning in real-world coding scenarios. Labeled nuanced data involving ambiguity, ethical considerations, and user intent, under rigorous quality standards supervised by peers.

AI Data Labeling Specialist — DataAnnotation (Freelance) Remote · 2024 – Present Contributed to a high-impact multilingual project for a leading AI research lab, focused on improving the reasoning, code generation, and translation capabilities of large language models (LLMs). As a bilingual engineer, I was selected for tasks requiring both Spanish–English fluency and programming proficiency to support the development of robust, globally deployable AI systems. Translated and annotated complex Spanish–English prompts and completions, ensuring cultural, grammatical, and contextual accuracy across diverse use cases (e.g., search queries, dialogue, summarization). Applied programming knowledge (Python, JavaScript, C++) to evaluate code completions, debug logic errors, and design prompts that test reasoning in real-world coding scenarios. Labeled nuanced data involving ambiguity, ethical considerations, and user intent, under rigorous quality standards supervised by peers.

2024

Education

Work History