Nathan B.

Key Skills

Software

Other

Top Subject Matter

Generative AI Model Evaluation

NLP Dataset Annotation and QA

Top Data Types

Text

Top Task Types

RLHF

Entity (NER) Classification

Freelancer Overview

AI Data Trainer – Independent Contractor. Brings 7+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal, Proprietary Tooling, and Other. Education includes Bachelor of Science, University of California, Irvine (2020) and Associate of Science, El Camino College (2018). AI-training focus includes data types such as Text and labeling workflows including RLHF and Entity (NER) Classification.

Labeling Experience

AI Data Trainer – Independent Contractor

TextRLHF

Evaluated and refined generative AI model outputs using reinforcement learning from human feedback, focusing on improving response quality and alignment. Conducted structured prompt-response validation, hallucination detection, and generated feedback reports to support iterative model development. Maintained high annotation accuracy and collaborated on systematic QA initiatives targeting reasoning quality and diverse output categories. • Applied RLHF techniques to large-scale generative AI tasks • Performed multi-step quality assurance and reasoning performance analysis • Produced structured, team-adopted feedback documentation • Drove improvement on accuracy, coherence, and safety benchmarks.

2022 - Present

Data Annotation & AI Quality Specialist – Freelance

OtherTextEntity (NER) Classification

Conducted high-volume text annotation for machine learning pipelines, focusing on tasks such as sentiment, intent, and entity classification. Reviewed and validated AI-generated responses for factual accuracy, coherence, and linguistic quality, while proactively flagging labeling drift and bias patterns. Developed and implemented workflow documentation and validation procedures to improve team throughput and annotation efficiency. • Annotated structured NLP dataset components for model training • Evaluated text-based AI outputs for labeling quality and consistency • Led procedural improvements for annotation and QA workflows • Supported ingestion of high-quality labeled data into production pipelines.

2020 - 2022

Education

Work History