Principal AI​/Machine Learning Engineer

Position: Principal AI / Machine Learning Engineer

• * About

The Role

• * The Principal AI/Machine Learning Engineer will oversee defining and executing ZT’s roadmap for applying artificial intelligence and machine learning in manufacturing. The AI/ML Transformation Architect will be the pivotal role in shaping ZT’s future-state vision for AI & ML by identifying high-impact use cases, preparing the organization structurally and technically for adoption, and driving successful implementation of applications.
• * What You Will Do
• ** Lead or contribute to
• * transformation initiatives**, helping set new standards for how ZT approaches manufacturing risk analysis, quality, and continuous improvement.
• Partner with leadership to define the vision and strategy for AI/ML adoption across manufacturing operations.
• Work with factory engineering, quality, and operations to identify, evaluate, and prioritize AI/ML use cases that deliver measurable business value.
• Collaborate across
• * design, quality, manufacturing, test, and supplier engineering
• * to drive solutions that integrate seamlessly into production.
• Define and implement
• * new systems, processes, or frameworks
• * that support the smart factory vision, including automation, metrology, advanced inspection, and predictive analytics.
• Define the organizational, data, and process changes required to prepare the business for AI/ML integration.
• Drive the design, development, and deployment of AI/ML solutions, ensuring successful adoption across factories.
• Apply AI/ML techniques to analyze manufacturing data sets – including metrology, vision inspection, event data, test results – conduct regression analysis, correlation studies, and commonality analysis.
• Leverage
• * deep, data-rich environments
• * and tools (e.g., Minitab, JMP, Python, R, SQL) to generate insights that improve yield, reliability, and throughput.
• Apply
• * advanced statistical and analytical methods** (regression, correlation, DOE, SPC, PFMEA, Gauge R&R, commonality studies) to identify, quantify, and control risk in complex manufacturing environments.
• Champion the cultural and operational transformation required for AI/ML success, including training and upskilling the industrial engineering team in new methods and approaches for mathematical computing.
• Serve as the bridge between industrial engineering, factory engineering teams, quality, and IT on AI/ML initiatives.
• Coach and nurture data stakeholders to maximize their potential and facilitate a culture of learning and growth. Act as a thought partner and subject matter expert to refine ideas, generate hypotheses, and analyze data to formulate solutions.
• Demonstrate strong leadership and influence management skills, including the ability to challenge the status quo and manage key senior stakeholders.
• Use predictive analytics to inform
• * PFMEA analyses
• * that will result in actionable process controls, ensuring proactive prevention of variation rather than reactive correction.
• * What You Bring
• * The right person for this role is an agent of change and has exceptional analytical capabilities, thrives in a fast-paced environment, loves problem-solving, is a good communicator, and is passionate about enabling the future of cloud computing.
• Advanced degree in Engineering, Computer Science, Data Science, or a related field.
• 10–15 years of experience in high-volume, high-complexity manufacturing, with at least 5 years in leadership or transformation roles (not necessarily people management).
• Demonstrated expertise in statistical and analytical methods such as regression analysis, correlation analysis, DOE, SPC, PFMEA, Gauge R&R, and commonality studies.
• Fluency with data-driven tools such as Minitab, JMP, Python, R, SQL (or equivalent) to analyze and interpret large, complex datasets.
• Track record of driving measurable improvements in yield, reliability, or process robustness.
• Background in electronics assembly, PCBA, servers, or other high-reliability industries (e.g., aerospace, medical devices, automotive, etc.).
• Experience with applying
• * AI/ML toolsets
• * to statistical problem solving, predictive analytics, or anomaly detection
• Experience coaching or mentoring…