Neal Quesinberry

About Neal Quesinberry

I’m an AI Data Quality & Test Engineer specializing in computer vision QA, dataset validation, and ML training data integrity.At BigBear.ai, I perform quality validation and structured QA testing for large-scale computer vision programs, ensuring annotated datasets meet accuracy, consistency, and guideline compliance standards before progressing through downstream ML workflows.My work focuses on:• Designing and executing QA validation procedures for annotated datasets in CVAT• Performing first and second pass QA, including bounding box precision, track continuity, and taxonomy adherence• Identifying defect trends and conducting root cause analysis across labeling workflows• Supporting adjudication processes to resolve annotation ambiguity and improve standardization• Executing regression validation following guideline updates, schema changes, or tooling modifications• Using Python and SQL to audit datasets, validate metadata, and investigate quality anomalies• Tracking defect patterns and rework cycles to strengthen data operations quality controlsI approach dataset quality as a systems problem while emphasizing reproducibility, measurable validation criteria, and clear standards that reduce downstream model risk.Before transitioning into AI, I spent 15+ years designing structured learning systems for enterprise and government organizations. That background informs how I think about annotation guidelines, process clarity, and measurable performance — whether training humans or training machine learning systems.My career through line is consistent: high-quality inputs drive reliable outcomes.I’m particularly interested in advancing scalable data validation frameworks, ML quality governance, and structured QA methodologies within enterprise AI environments.

Experience

1. Data Labeling Quality Assurance, Testing & Operations Analyst (Test Engineer)

   BigBear.ai

   Feb 2026 — Present

   Lead dataset acceptance testing and quality validation for large-scale AI/ML computer vision programs.• Own dataset acceptance testing by designing and executing QA test plans and validation procedures before labeled data is released into downstream ML pipelines• Serve as the final quality gate for annotated datasets, ensuring accuracy, completeness, consistency, and guideline compliance in CVAT• Conduct first- and second-pass QA reviews, identify defect trends and root causes, and track rework through verification and closure• Lead peer review and adjudication workflows, documenting traceable decisions and maintaining QA standards• Execute regression testing following guideline updates, schema changes, or tooling modifications to protect model training integrity

Education

• Virginia Tech

  Master of Arts (MA), English

  1996 — 1999

• Virginia Tech

  Bachelor of Arts (BA), English

  1989 — 1995

Skills

• Training Delivery
• User Experience
• Gamification
• Webinar Development
• Learning Management
• Content Development
• Performance Improvement
• Instructional Technology
• Customer Service Training
• Performance Consulting
• Curriculum Design
• E-Learning
• Scenario Development
• Employee Training
• Virtual Learning
• Learning Management Systems
• Mlearning
• Addie
• Product Training
• Software Development Life Cycle (Sdlc)
• Adaptive Learning
• Educational Technology
• Change Management
• Technical Documentation
• Social Learning
• Learning Analytics
• Simulations
• Performance Benchmarking
• Technical Training
• Instructor-Led Training
• Organizational Learning
• User Scenarios
• Mobile Learning
• Responsive Web Design
• Management
• Leadership Development
• Blended Learning
• Multimedia
• Moodle
• Customer Product Training