Senior QA Validation Engineer for AI Initiatives (Teradyne, North Reading, MA)

We are the global test and automation specialists, powering next-generation technologies through sophisticated solutions. Behind every electronic device you use, Teradyne's test technology ensures your device works right the first time, every time! Our portfolio of automation solutions help manufacturers to develop and deliver products quickly, efficiently and cost-effectively. Together, Teradyne companies deliver manufacturing automation across industries and applications around the world!

We attract, develop, and retain a high-performance workforce, comprised of people with diverse backgrounds and a shared drive for excellence. We strive to foster a positive and inclusive work environment that helps employees, and communities, thrive.

Our Purpose **
TERADYNE, where experience meets innovation and driving excellence in every connection. We are fueled by creativity and diversity of thought and in our workforce. Our employees are supported to innovate and learn something new every day.

We cultivate a culture of inclusion for all employees that respects their individual strengths, views, and experiences. We believe that our differences enable us to be a better team – one that makes better decisions, drives innovation and delivers better business results.

Opportunity Overview

The QA/Validation Lead Engineer for AI Initiatives is responsible for leading the end-to-end validation, testing, and quality assurance of all AI/ML systems from a data-centric perspective. This role ensures that proper data sources, structures, pipelines, and governance frameworks are validated and maintained across all AI initiatives, including generative AI, agentic systems, and human-in-the-loop workflows. The role combines strategic QA leadership with hands-on technical validation to safeguard the accuracy, reliability, compliance, and ethical use of data powering AI solutions. This position reports to the IT Quality Assurance Manager.

• Validate that AI/ML models are consuming accurate, authorized, and properly structured data sources.

• Design and execute data quality test strategies to assess completeness, consistency, lineage, and timeliness of training and inference data.

• Identify and flag data hallucinations, logic errors, and edge cases in AI model outputs traceable to data issues.

• Develop and lead comprehensive test strategies for AI/ML systems, including accuracy, bias, robustness, and regression testing.

• Oversee scenario-based testing and output validation for generative AI and LLM-driven applications.

• Automate validation suites for agentic/multi-agent systems, integration testing, and CI/CD pipelines for ML models.