Diego R.

Key Skills

Software

Appen

Data Annotation Tech

CrowdSource

Datumbox

V7 Labs

Top Subject Matter

Data Analyst

Machine Learning Engineer

AI Domain Expert

Top Data Types

Text

Document

Computer Code Programming

Top Task Types

Text Summarization

Question Answering

Fine-tuning

Transcription

Evaluation/Rating

Computer Programming/Coding

Data Collection

Freelancer Overview

My path into AI training data work grew naturally out of years spent building and evaluating ML systems in production. As an AI Engineer at TechNova Solutions and previously as a Data Analyst at Insight Analytics Group, I repeatedly found myself doing the work at the heart of data labeling: judging model outputs, catching edge cases, writing guidelines others could apply consistently, and translating complex AI behavior into clear, auditable reasoning. Across NLP, deep learning, and computer vision projects, I've developed a sharp instinct for what makes a model response accurate, well-reasoned, and trustworthy. I completed my PhD at Ohio State in 2025, with a dissertation on adaptive architectures for heterogeneous real-world data — which deepened my understanding of exactly where and why models fail, and what high-quality training signals need to address. Combined with certifications in AWS ML, Google Data Engineering, and Azure AI, and active contributions to open-source ML tooling, I bring both the domain expertise and the practitioner's eye to produce training data that meaningfully moves model quality forward.

Labeling Experience

AI Model Evaluation and NLP Annotation

TextClassification

Worked on evaluation and annotation tasks for NLP and AI systems, including classification, response quality assessment, factual accuracy review, and edge-case identification. Reviewed model outputs for correctness, clarity, reasoning quality, and policy compliance, and provided structured judgments to improve training data quality. Applied strong domain knowledge in AI, machine learning, and technical communication to assess complex text and write precise, consistent annotations.

2024 - Present

AI Model Evaluation and NLP Annotation

TextClassification

Worked on evaluation and annotation tasks for NLP and AI systems, including classification, response quality assessment, factual accuracy review, and edge-case identification. Reviewed model outputs for correctness, clarity, reasoning quality, and policy compliance, and provided structured judgments to improve training data quality. Applied strong domain knowledge in AI, machine learning, and technical communication to assess complex text and write precise, consistent annotations.

2024 - Present

Education

Work History