Israel Lozano

Key Skills

Software

Data Annotation Tech

Mindrift

OneForma

Remotasks

Scale AI

Other

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Image

Text

Top Task Types

Classification

Data Collection

Evaluation Rating

Object Detection

Prompt Response Writing SFT

Freelancer Overview

I have extensive experience in AI training and data labeling, specializing in evaluation and generation of text in both English and Spanish. My work includes providing high-quality feedback on AI-generated responses, focusing on factual accuracy, relevance, coherence, and the detection of harmful or biased content. I have collaborated on large-scale projects requiring linguistic sensitivity, deep cultural awareness, and the ability to identify nuanced issues across contexts. My background in literature, photography, and editorial work gives me a strong command of language, narrative structure, and tone adaptation—skills that are essential when curating or evaluating prompts, responses, or training datasets. What sets me apart is my bilingual fluency, attention to detail, and ability to work across disciplines. I have contributed to the development of AI systems by crafting and calibrating prompts, rating outputs, and ensuring alignment with ethical guidelines. I also bring a creative and analytical mindset shaped by my work as an educator, artist, and writer, allowing me to evaluate content not only for correctness but also for emotional resonance and human impact.

Labeling Experience

Personalization

Scale AITextClassification

I participated in a data labeling project focused on evaluating the personalization capabilities of a chatbot system. The task involved analyzing user-chatbot conversations and categorizing the personalization elements present in the responses. We were instructed to classify the prompts into four categories: name, interest, gender, and memory. Our role was to identify whether the chatbot demonstrated memory—such as recalling the user's name, preferences, or previous statements—without the user having to repeat them in each interaction. This required a deep understanding of dialogue flow, contextual relevance, and user-specific references. Each annotation contributed to improving the model’s ability to maintain coherent, personalized interactions across sessions, enhancing the overall user experience. The project demanded precision, consistency, and linguistic sensitivity in both English and Spanish.

I participated in a data labeling project focused on evaluating the personalization capabilities of a chatbot system. The task involved analyzing user-chatbot conversations and categorizing the personalization elements present in the responses. We were instructed to classify the prompts into four categories: name, interest, gender, and memory. Our role was to identify whether the chatbot demonstrated memory—such as recalling the user's name, preferences, or previous statements—without the user having to repeat them in each interaction. This required a deep understanding of dialogue flow, contextual relevance, and user-specific references. Each annotation contributed to improving the model’s ability to maintain coherent, personalized interactions across sessions, enhancing the overall user experience. The project demanded precision, consistency, and linguistic sensitivity in both English and Spanish.

2024

Education

Work History