Advanced Manufacturing Engineer(iPhone) - Smart Manufacturing

As a lead engineer on our manufacturing team, you will guide technical automation projects for iPhone final assembly from early concept through end-of-life production. In this unique role, you will bridge the gap between traditional hardware engineering and next-generation smart manufacturing. You will apply your deep expertise in mechanical product design and automation while leveraging artificial intelligence, machine learning, and Large Language Models (LLMs) to unlock new efficiencies, optimize manufacturing processes, and establish data-driven standard practices. By mentoring cross-functional teams and factory technicians, you will ensure our designs meet rigorous manufacturability and quality standards. This is an environment where your attention to detail, passion for excellence, and forward-thinking approach to AI and automation will directly influence the future of our manufacturing capabilities. Minimum Qualifications Bachelor's or Master's degree in Mechanical Engineering, Automation, Computer Science, Data Science, or equivalent experience. 5+ years of engineering experience in an automation or smart manufacturing role. Practical coding experience (e.g., Python, R, SQL) with a demonstrated ability to apply Artificial Intelligence (AI) or Machine Learning (ML) techniques to solve engineering or production problems. Experience directing an engineering team through new product introductions. Experience with project management, cost reduction, and mechanical engineering design. Fluent in both written and spoken English and Mandarin. Preferred Qualifications Knowledge of robot arm applications, calibration processes, pneumatics, servo motors, and programmable logic controller (PLC) systems, and how to integrate them with edge computing or IoT data pipelines. Understanding of optics principles for 2D and 3D vision systems, positioning, and critical alignment fixtures, ideally combined with AI-driven computer vision (CV). Familiarity with adhesives and dispensing technologies. Experience using statistical analysis tools (e.g., JMP, Minitab, Six Sigma) alongside advanced methodologies (e.g., Machine Learning, deep learning, LLMs). Proven track record of leveraging LLMs or Generative AI tools to automate engineering workflows, summarize technical documents, or build smart factory applications. Experience communicating technical information across global, cross-functional teams, specifically bridging the gap between traditional hardware manufacturing and software/data science teams.