Walter F.

Key Skills

Software

Scale AI

Top Subject Matter

Large Language Model (LLM) training data preparation and annotation quality for machine learning.

Enterprise AI dataset annotation and validation (image, text, and NLP) for machine learning training.

Top Data Types

Text

Image

Top Task Types

RLHF

Entity (NER) Classification

Freelancer Overview

Senior Data Modeler / AI Trainer. Brings 4+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include DataAnnotation.tech and Scale AI. Education includes Bachelor of Science, Hofstra University (2019). AI-training focus includes data types such as Text and labeling workflows including RLHF and Entity (NER) Classification.

Labeling Experience

Senior Data Modeler / AI Trainer

TextRLHF

Performed LLM training data creation work by building and maintaining structured annotation frameworks to support model learning. Implemented data validation processes to improve label consistency and reduce annotation errors across training datasets. Reviewed AI-generated outputs and applied quality metrics to evaluate and improve dataset and model reliability. • Created structured datasets and annotation frameworks for LLM training. • Developed validation workflows to increase dataset consistency and reduce labeling mistakes. • Set quality metrics during AI output review to boost performance and reliability. • Defined data standards, metadata documentation, and labeling best practices.

2025 - Present

Data Annotation Specialist

Scale AITextEntity (NER) Classification

Annotated and validated large-scale image, text, and NLP datasets to support machine learning model development. Achieved high annotation accuracy (98%) across multiple enterprise AI projects through careful quality checks and refinement of labeling standards. Identified data quality issues, inconsistencies, and edge cases to improve the reliability of training datasets. • Labeled and validated image, text, and NLP datasets for ML training. • Maintained 98% annotation accuracy across enterprise AI projects. • Flagged data quality problems, inconsistencies, and edge cases. • Supported data governance via documentation and quality assurance reviews.

2019 - 2022

Education

Work History