Product Engineering develops and establishes many of the processes used throughout product development, qualification, and manufacturing. As product complexity, engineering data, and production volumes continue to grow, these processes must evolve into connected, traceable, and AI-ready systems.

The Photonic Product Engineer for AI & Automation is responsible for executing Product Engineering's AI and automation initiatives and driving adoption across the organization. The role works closely with Development, Product Engineering, Test, Reliability, Quality, Operations, Manufacturing, and IT teams to establish common methods, reusable tools, and scalable practices that improve engineering effectiveness and support future growth.

Success in this role is measured not only by technical solutions delivered, but by the extent to which AI, automation, and data-driven methods become embedded within everyday engineering practice.

 

The position is based in Sunnyvale, Ca.

 

• PhD in Electrical Engineering, Computer Science, Data Science, Physics, Photonics, Applied Mathematics, or related field.
• Experience applying AI, machine learning, analytics, software, or automation to engineering, product development, test, or manufacturing challenges.
• Strong programming, data analysis, and tool development skills using Python or similar technologies.
• Experience with AI technologies, engineering data systems, knowledge management, traceability, or decision-support tools.
• Experience with optical transceiver products, photonics, DSP, semiconductor technologies, Product Engineering processes, or manufacturing and test systems is highly desirable.

• Execute Product Engineering AI and automation initiatives across PIC, ASIC, TROSA, and Plug.
• Drive adoption of AI, ML, automation, and data-driven engineering practices within Prod Eng and partner organizations.
• Establish common methods, frameworks, and knowledge systems that enable scalable use of AI across the product lifecycle.
• Transform engineering data, workflows, and traceability into AI-ready systems spanning development, qualification, manufacturing, reliability, and field performance.
• Build, validate, and deploy reusable tools, agents, analytics, and automation solutions that improve engineering effectiveness.
• Partner with Development, Test, Reliability, Quality, Manufacturing, Operations, and IT teams to align data, tools, and practices and deliver measurable business impact.