Structured Output Review
I evaluate whether outputs meet technical requirements, user expectations, and real-world usefulness.
Software QA • AI Model Evaluation • Remote Roles
Testing complex systems. Evaluating AI outputs. Turning ambiguity into actionable findings.
I’m Michael R. Williams, a Software Quality Engineer with more than twenty years of experience evaluating complex system behavior, identifying defects, analyzing edge cases, and helping teams deliver reliable software.
I am now focused on remote roles in AI model evaluation, quality analysis, and related areas where structured testing, careful judgment, and clear communication matter.
A QA background built for AI evaluation
AI systems do not always behave predictably. They require more than simple pass/fail validation. They require structured evaluation, careful review of inconsistent outputs, and the ability to translate ambiguous behavior into clear, useful feedback.
Failure Pattern Detection
I look for inconsistencies, hallucinations, edge-case failures, unclear responses, and reliability issues.
Clear Actionable Findings
I turn vague or inconsistent behavior into practical reports that product, engineering, and evaluation teams can use.
Hands-on generative AI experience
I have actively worked with generative AI tools including ChatGPT, Nano Banana, SeeDream, Grok, and others to generate, evaluate, and refine outputs across technical and creative use cases.
Prompt Iteration
Developing and refining prompts to improve quality, clarity, consistency, and usefulness of generated outputs.
Quality Assessment
Reviewing AI output for accuracy, relevance, coherence, and potential failure modes.
Practical AI Application
Using multiple AI tools to develop product designs and marketing content for a niche print-on-demand business.
Core competencies
My strengths combine traditional software quality engineering with the type of analytical review needed for AI evaluation work.
Test Case Development
Root Cause Analysis
Requirement Development
Technical Documentation
Problem Troubleshooting
Issue Triage
SQL and Data Analysis
Project Metrics
Mentoring
Edge Case Analysis
Systems tested and supported
My work has covered retail, telecom, government, logistics, web applications, data warehousing, command-center systems, and network control environments.
Point-of-sale user interface software for Salesforce.com customers including Les Schwab, NAPA, Hallmark, Party City, and others.
Verizon internal order processing and government ordering systems.
Customer-facing applications on the EchoStar / DISH Network website.
Asset management and tracking systems at WorldCom.
Network data warehousing, control, and monitoring systems at MCI.
Logistics systems for the Royal Saudi Air Force.
Space Forecast Center and Wing Command Center systems at Schriever Air Force Base.
Small vehicle dealer warranty processing and software-based manuals with interactive exploded parts views.
Professional experience
More than two decades of software quality, testing, engineering, technical leadership, and systems analysis experience.
2021 – 2023
Medical Leave and Recovery
Returned with a renewed focus on applying software quality expertise to AI evaluation and analysis roles.
2011 – 2021
Salesforce.com / Demandware / Tomax, Inc.
Principal Software QA Engineer / QA Lead — Salt Lake City, Utah
2009 – 2010
Independent Software Engineering
Software Engineer — Colorado Springs, Colorado
2007 – 2009
Sapphire Technology at Verizon Business
Contract Software Engineer — Colorado Springs, Colorado
2006 – 2007
EchoStar
Test Engineer — Englewood, Colorado
2003 – 2006
L&M Sales
Software Engineer — Colorado Springs, Colorado
1999 – 2002
WorldCom
Software Lead — Colorado Springs, Colorado
1997 – 1999
ISTC, Inc.
Corporate President / Principal Test Engineer — Colorado Springs, Colorado
1991 – 1997
MCI
Lead Test Engineer — Colorado Springs, Colorado
Let’s talk about AI quality.
I am especially interested in roles where I can contribute to improving model reliability, identifying failure patterns, and helping ensure that AI systems deliver accurate, useful, and responsible outputs.