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Ron Bolden

Senior AI Training Specialist / LLM Alignment Engineer

USA flag
Round Rock, Usa
$20.00/hrExpertAws SagemakerArgillaClickworker

Key Skills

Software

AWS SageMakerAWS SageMaker
ArgillaArgilla
ClickworkerClickworker
CrowdFlowerCrowdFlower
CloudFactoryCloudFactory
TolokaToloka
TelusTelus
Scale AIScale AI

Top Subject Matter

Technology – Enterprise Software & Cloud Infrastructure
Manufacturing – Quality Control & Industrial IoT
Finance – Risk Analysis & Fraud Detection

Top Data Types

ImageImage
VideoVideo
TextText

Top Task Types

RLHF
Segmentation
Entity Ner Classification
Evaluation Rating
Polygon
Object Detection
Data Collection

Freelancer Overview

Principal Software Engineer. Brings 27+ years of professional experience across complex professional workflows, research, and quality-focused execution. Education includes Master of Science, University of Texas at Dallas (2014) and Bachelor of Science, Texas A&M University (1997).

ExpertEnglish

Labeling Experience

Senior AI Training Specialist: RLHF & Instruction Tuning

TextRLHF
Orchestrated the end-to-end data labeling strategy for a suite of enterprise-grade Large Language Models (LLMs) at Dell Technologies. Managed a pipeline of 50,000+ instruction-response pairs focused on IT infrastructure troubleshooting and hardware configuration. Scope: Led the curation and annotation of high-fidelity datasets specifically designed for Reinforcement Learning from Human Feedback (RLHF). This involved designing complex rubrics for labelers to evaluate factual accuracy, harmlessness, and stylistic nuance against proprietary technical documentation. Tasks: Specialized in "ranking" annotation types, where annotators were guided to compare multiple model outputs. Developed Python scripts using PyTorch to perform automated quality checks (consensus analysis) on labeled data, identifying annotator drift and correcting labeling inconsistencies in real-time. Quality Measures: Implemented a "golden set" validation protocol. By injecting known-correct answers into the labeling queue, we maintained an inter-annotator agreement (IAA) score of >92%. This project directly resulted in a 22% reduction in hallucination rates for internal customer-support AI agents.

Orchestrated the end-to-end data labeling strategy for a suite of enterprise-grade Large Language Models (LLMs) at Dell Technologies. Managed a pipeline of 50,000+ instruction-response pairs focused on IT infrastructure troubleshooting and hardware configuration. Scope: Led the curation and annotation of high-fidelity datasets specifically designed for Reinforcement Learning from Human Feedback (RLHF). This involved designing complex rubrics for labelers to evaluate factual accuracy, harmlessness, and stylistic nuance against proprietary technical documentation. Tasks: Specialized in "ranking" annotation types, where annotators were guided to compare multiple model outputs. Developed Python scripts using PyTorch to perform automated quality checks (consensus analysis) on labeled data, identifying annotator drift and correcting labeling inconsistencies in real-time. Quality Measures: Implemented a "golden set" validation protocol. By injecting known-correct answers into the labeling queue, we maintained an inter-annotator agreement (IAA) score of >92%. This project directly resulted in a 22% reduction in hallucination rates for internal customer-support AI agents.

2022 - Present

Data Labeling Consultant: Structured Data & Differential Privacy

TextEntity Ner Classification
Led a data labeling initiative to create a privacy-preserving training dataset for automated security auditing tools. This project utilized my expertise in differential privacy and multi-tenant cloud infrastructure to transform raw server logs into structured training data. Scope: Processed and labeled 10,000+ hours of unstructured log data (from AWS S3 and EC2 environments) to classify critical vulnerabilities. The final dataset was used to train a model that automated the detection of 150+ vulnerability types. Tasks: Executed complex Named Entity Recognition (NER) labeling to extract IP addresses, user behaviors, and threat signatures from raw text. Designed the "taxonomy" for classification labels, ensuring that the ontology matched both Common Vulnerabilities and Exposures (CVE) standards and internal enterprise security requirements. Quality Measures: Implemented a secure multi-party computation (SMPC) workflow to validate labels without exposing sensitive customer data to raw text viewers. Utilized statistical machine learning techniques to audit labeling consistency across the team, ensuring that the resulting structured dataset maintained high integrity for fine-tuning security-specific LLMs.

Led a data labeling initiative to create a privacy-preserving training dataset for automated security auditing tools. This project utilized my expertise in differential privacy and multi-tenant cloud infrastructure to transform raw server logs into structured training data. Scope: Processed and labeled 10,000+ hours of unstructured log data (from AWS S3 and EC2 environments) to classify critical vulnerabilities. The final dataset was used to train a model that automated the detection of 150+ vulnerability types. Tasks: Executed complex Named Entity Recognition (NER) labeling to extract IP addresses, user behaviors, and threat signatures from raw text. Designed the "taxonomy" for classification labels, ensuring that the ontology matched both Common Vulnerabilities and Exposures (CVE) standards and internal enterprise security requirements. Quality Measures: Implemented a secure multi-party computation (SMPC) workflow to validate labels without exposing sensitive customer data to raw text viewers. Utilized statistical machine learning techniques to audit labeling consistency across the team, ensuring that the resulting structured dataset maintained high integrity for fine-tuning security-specific LLMs.

2020 - 2023

Lead Annotation Architect: Edge Vision & Synthetic Data

ImageSegmentation
Directed a high-stakes data labeling initiative for a neural architecture search (NAS) project aimed at optimizing computer vision models for manufacturing quality control (FPGA-based systems). Scope: Managed the creation of a dataset containing 1.2 million annotated images. The project utilized synthetic data generation (Unreal Engine) combined with human-labeled "edge cases" to train models for defect detection in low-bandwidth IoT environments, aligning with my graduate thesis work. Tasks: Focused on complex polygon annotation and semantic segmentation for microscopic hardware defects that were previously undetectable by traditional software. Authored technical guidelines (Labeling Interface Specs) for a team of 15 remote annotators, ensuring precise boundary detection for sub-millimeter components. Quality Measures: Leveraged my background in CUDA programming and HPC to build a statistical sampling framework that automatically flagged low-confidence annotations for manual review. This approach reduced rework costs by 30% while maintaining >98% label accuracy, enabling the final model to reduce parameter count by 15% without sacrificing inference accuracy.

Directed a high-stakes data labeling initiative for a neural architecture search (NAS) project aimed at optimizing computer vision models for manufacturing quality control (FPGA-based systems). Scope: Managed the creation of a dataset containing 1.2 million annotated images. The project utilized synthetic data generation (Unreal Engine) combined with human-labeled "edge cases" to train models for defect detection in low-bandwidth IoT environments, aligning with my graduate thesis work. Tasks: Focused on complex polygon annotation and semantic segmentation for microscopic hardware defects that were previously undetectable by traditional software. Authored technical guidelines (Labeling Interface Specs) for a team of 15 remote annotators, ensuring precise boundary detection for sub-millimeter components. Quality Measures: Leveraged my background in CUDA programming and HPC to build a statistical sampling framework that automatically flagged low-confidence annotations for manual review. This approach reduced rework costs by 30% while maintaining >98% label accuracy, enabling the final model to reduce parameter count by 15% without sacrificing inference accuracy.

2019 - 2022

Education

U

University of Texas at Dallas

Master of Science, Computer Science

Master of Science
2012 - 2014
T

Texas A&M University

Bachelor of Science, Computer Engineering

Bachelor of Science
1993 - 1997

Work History

D

Dell Technologies

Principal Software Engineer

Round Rock
2018 - Present
R

Rackspace Technology

Senior Systems Architect

San Antonio
2008 - 2017