Responsibilities include:
• Lead ML-based tool development to optimize engineering qualification (EQ) scheduling and
enable data-driven gap analysis.
• Drive LLM-based workflow automation initiatives across the reliability and engineering
program management organization.
• Support network integration of hardware reliability testers, including software development,
implementation, and configuration of hardware infrastructure.
• Collaborate on the development and implementation of computer vision-based algorithms for
reliability and quality assessments.
• Partner closely with cross-functional teams (reliability, software, data engineering,
infrastructure) to identify and implement automation opportunities.
• Provide technical leadership in defining scalable and sustainable solutions that support
engineering and test operations.
Key Qualifications
• Proven experience managing AI/ML-based engineering tools or platforms, particularly for
scheduling optimization, anomaly detection, or workflow automation.
• Hands-on experience working with Large Language Models (LLMs), including prompt design,
model integration, or automation of team workflows.
• Strong understanding of software development and data infrastructure, with experience
supporting network-connected hardware systems.
• Familiarity with computer vision algorithms and their application in engineering or testing
environments.
• Demonstrated ability to lead complex, cross-functional technical projects across diverse global
teams.
• Excellent problem-solving and analytical skills with a deep sense of ownership and technical
curiosity.
• Strong interpersonal and communication skills; able to drive alignment and engagement
across engineering, product, and infrastructure teams.
• Professional proficiency in both Chinese and English, with an ability to operate effectively in
bilingual environments