Ines Salcedo

Key Skills

Software

Appen

Data Annotation Tech

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Document

Text

Top Task Types

Classification

Computer Programming Coding

Evaluation Rating

Prompt Response Writing SFT

Freelancer Overview

I have hands-on experience in data labeling and AI training data, particularly within natural language processing (NLP) projects focused on multilingual and culturally nuanced content. As part of Project Fireweed, I contributed to the fine-tuning of a Large Language Model (LLM) by performing high-quality annotations in both English and Spanish. My responsibilities included localization ranking—evaluating how contextually local content was—and generating comprehension-based questions and answers aligned with strict linguistic guidelines. A key aspect of the role involved ensuring accurate translation and cultural relevance, which strengthened the model's performance across language domains. What sets me apart is my ability to work bilingually with a sharp eye for contextual accuracy, cultural nuance, and linguistic consistency. I bring a structured, quality-driven approach to annotation and content evaluation, with a strong understanding of how high-quality data improves model performance. My background enables me to bridge the gap between technical AI training requirements and real-world language use, ensuring that models are both functionally robust and culturally adaptive.

Labeling Experience

Freelance NLP Annotator – Project Fireweed

AppenTextTranslation Localization

Worked as a bilingual annotator supporting the development of a multilingual LLM. Tasks included creating prompts, evaluating AI-generated responses across six quality dimensions (e.g., coherence, factuality, tone), and identifying the preferred output with written justifications. I also revised or rewrote responses to improve grammar, fluency, formatting, and localization accuracy in both English and Spanish. The project spanned a wide range of domains and followed strict annotation guidelines and quality standards, contributing to a large-scale, collaborative effort to fine-tune state-of-the-art language models.

Worked as a bilingual annotator supporting the development of a multilingual LLM. Tasks included creating prompts, evaluating AI-generated responses across six quality dimensions (e.g., coherence, factuality, tone), and identifying the preferred output with written justifications. I also revised or rewrote responses to improve grammar, fluency, formatting, and localization accuracy in both English and Spanish. The project spanned a wide range of domains and followed strict annotation guidelines and quality standards, contributing to a large-scale, collaborative effort to fine-tune state-of-the-art language models.

2024 - 2024

Education

Work History