Advanced Manufacturing Engineer(iPhone) - Smart Manufacturing
Apple
24 hours ago
On-site
Suzhou, Jiangsu, United States
The people here at Apple don’t just build products — they build the kind of wonder that’s revolutionized entire industries. In this role, you will drive manufacturing research and development for iPhone final assembly line automation, pioneering the integration of cutting-edge AI/ML and smart manufacturing technologies. You will solve complex challenges that directly shape the quality, cost, and delivery of our products. By collaborating deeply across engineering groups, you will turn innovative concepts into extraordinary customer experiences and help define the factory of the future.\\n
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.
Drive Smart Manufacturing Innovation: Spearhead the adoption of AI/ML, Large Language Models (LLMs), and advanced data analytics to revolutionize traditional AME workflows—ranging from predictive automation performance modeling and automated root-cause analysis to intelligent reporting.\\nLead Core Engineering Projects: Direct manufacturing engineering projects from concept through production, focusing on design for manufacturability, cost reduction, and continuous improvement for final assembly.\\nDevelop Intelligent Processes: Develop and implement sophisticated, data-driven manufacturing processes and intelligent testing methods that leverage machine learning to optimize production efficiency and product quality.\\nAdvanced Data \u0026 Failure Analysis: Conduct failure and data analysis on upcoming products, utilizing a combination of traditional statistical tools and modern AI-driven insights to drive design changes and final assembly standards.\\nMechanical Design Evaluation: Evaluate and define mechanical design approaches, including creating and managing tolerance stack-ups for assemblies and individual components, enhanced by computational modeling and data trends.\\nGlobal Mentorship \u0026 Technical Direction: Provide technical direction and mentorship to engineering teams, project leaders, and factory technicians globally, serving as an evangelist for AI-augmented manufacturing practices.
Bachelor"s or Master"s degree in Mechanical Engineering, Automation, Computer Science, Data Science, or equivalent experience.\\n5+ years of engineering experience in an automation or smart manufacturing role.\\nPractical 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.\\nExperience directing an engineering team through new product introductions.\\nExperience with project management, cost reduction, and mechanical engineering design.\\nFluent in both written and spoken English and Mandarin.\\n
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.\\nUnderstanding of optics principles for 2D and 3D vision systems, positioning, and critical alignment fixtures, ideally combined with AI-driven computer vision (CV).\\nFamiliarity with adhesives and dispensing technologies.\\nExperience using statistical analysis tools (e.g., JMP, Minitab, Six Sigma) alongside advanced methodologies (e.g., Machine Learning, deep learning, LLMs).\\nProven track record of leveraging LLMs or Generative AI tools to automate engineering workflows, summarize technical documents, or build smart factory applications.\\nExperience communicating technical information across global, cross-functional teams, specifically bridging the gap between traditional hardware manufacturing and software/data science teams.
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.
Drive Smart Manufacturing Innovation: Spearhead the adoption of AI/ML, Large Language Models (LLMs), and advanced data analytics to revolutionize traditional AME workflows—ranging from predictive automation performance modeling and automated root-cause analysis to intelligent reporting.\\nLead Core Engineering Projects: Direct manufacturing engineering projects from concept through production, focusing on design for manufacturability, cost reduction, and continuous improvement for final assembly.\\nDevelop Intelligent Processes: Develop and implement sophisticated, data-driven manufacturing processes and intelligent testing methods that leverage machine learning to optimize production efficiency and product quality.\\nAdvanced Data \u0026 Failure Analysis: Conduct failure and data analysis on upcoming products, utilizing a combination of traditional statistical tools and modern AI-driven insights to drive design changes and final assembly standards.\\nMechanical Design Evaluation: Evaluate and define mechanical design approaches, including creating and managing tolerance stack-ups for assemblies and individual components, enhanced by computational modeling and data trends.\\nGlobal Mentorship \u0026 Technical Direction: Provide technical direction and mentorship to engineering teams, project leaders, and factory technicians globally, serving as an evangelist for AI-augmented manufacturing practices.
Bachelor"s or Master"s degree in Mechanical Engineering, Automation, Computer Science, Data Science, or equivalent experience.\\n5+ years of engineering experience in an automation or smart manufacturing role.\\nPractical 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.\\nExperience directing an engineering team through new product introductions.\\nExperience with project management, cost reduction, and mechanical engineering design.\\nFluent in both written and spoken English and Mandarin.\\n
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.\\nUnderstanding of optics principles for 2D and 3D vision systems, positioning, and critical alignment fixtures, ideally combined with AI-driven computer vision (CV).\\nFamiliarity with adhesives and dispensing technologies.\\nExperience using statistical analysis tools (e.g., JMP, Minitab, Six Sigma) alongside advanced methodologies (e.g., Machine Learning, deep learning, LLMs).\\nProven track record of leveraging LLMs or Generative AI tools to automate engineering workflows, summarize technical documents, or build smart factory applications.\\nExperience communicating technical information across global, cross-functional teams, specifically bridging the gap between traditional hardware manufacturing and software/data science teams.