Principal, Quality Engineering- AI Lead

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Harris is dedicated to recruiting and developing high-performing talent who are passionate about what they do. Our employees are unified in a shared dedication to our customers’ mission and quest for professional growth. L3

Harris provides an inclusive, engaging environment designed to empower employees and promote work-life success. Fundamental to our culture is an unwavering focus on values, dedication to our communities, and commitment to excellence in everything we do.

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Harris is the Trusted Disruptor in defense tech. With customers’ mission-critical needs always in mind, our employees deliver end-to-end technology solutions connecting the space, air, land, sea and cyber domains in the interest of national security.

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Harris Missile Solutions is seeking a Leader for Artificial Intelligence (AI) and Machine Learning (ML) Quality to develop and execute the segment’s strategy for AI/ML governance, verification, and validation. Reporting to the Director of Quality Engineering, this role defines quality frameworks ensuring AI/ML models, computer vision systems, and automated inspection technologies meet mission-critical requirements. The position partners closely with Executive Leadership, Data Science, Software Engineering, Operations, and site Quality teams to support the safe deployment and lifecycle management of AI-enabled tools.

The ideal candidate brings experience implementing quality processes for software-intensive or AI-driven systems and leading cross-functional technical initiatives.

• Develop and implement AI/ML quality strategies and validation frameworks to assess model and software behavior across Missile Solutions applications.
• Establish and maintain data governance practices, including data quality controls, labeling accuracy, drift monitoring, and bias prevention.
• Define measurable performance metrics and acceptance criteria for AI applied to mission-critical or defect-detection systems.
• Lead verification and validation (V&V) activities for AI/ML models, including adversarial testing, stress testing, and edge-case simulation.
• Adapt traditional quality and risk tools to intelligent systems, including FMEA development and mitigation planning for machine-learning-specific failure modes.
• Oversee quality frameworks for automated inspection systems, including validation of sensor calibration, metrology data ingestion, and synthetic data pipelines.
• Govern post-deployment quality practices such as regression testing, drift thresholds, and monitoring/retraining workflows.
• Align internal AI engineering practices with emerging standards (e.g., ISO/IEC 42001, NIST AI RMF).
• Ensure compliance with AS9100, internal quality systems, and customer-specific quality requirements.
• Ability to obtain and maintain a U.S. Security Clearance.
• Up to 30% travel may be required.

• Bachelor’s Degree in Engineering, Computer Science, Data Science, Statistics, or related technical field with 12 years of prior relevant experience; OR Graduate Degree with 10 years of prior related experience; OR in lieu of a degree, 16 years of related experience.
• Minimum 3 years of experience leading technical projects, cross-functional initiatives, or engineering teams.
• Minimum 1 year of experience in verification, validation, testing, or quality management of software-intensive systems, AI/ML models, or analytics pipelines.