We are seeking highly motivated engineers with excellent communication skills to support end-to-end product development from early architecture and specification through NPI and high-volume manufacturing. This role spans design interaction, yield engineering, test plan optimization, and manufacturing scalability, requiring deep technical expertise in III-V devices, strong data-driven decision-making, and cross-functional leadership. This role bridges device-level results to final module , performance and spans DVT validation to yield improvement. The position provides a rich learning environment in a cross-functional team of PEs covering PICs, ASICs, Trosa and Modules and interfacing with hardware design, test, firmware/controls, reliability, and manufacturing team. The ideal candidate has strong Photonic Engineering background, excels at data analysis, and enjoys a multi-disciplinary environment working to drive product solutions into place. The position is based in Sunnyvale, CA. NION2026 Translate system requirements into detailed PIC specifications and ensure compliance through design, test, and Design Validation Test (DVT)Ensure products comply with specifications and capable of meeting yield targetsSupport proto line engineering on selection and delivery of development, proto, and pilot unitsPartner with Test Engineering to define test plans to comply with PIC specificationsPartner with Design Engineering on design variant performance analysis and manufacturing down-selection Track, troubleshoot, and support resolving PIC issues in collaboration with the design team.Evaluate capabilities, yields in preparation for volume ramp following product release.Transfer the product into manufacturing and support pilot yield and pareto analysis Ph.D. in Electrical Engineering or Physics involving photonics or semiconductorsExpertise in III-V or related optoelectronic active device design, fabrication and testTeam oriented, strong analytical skills, data driven decision making and excellent communication skills Fluent in larger data analysis using SQL, JMP or Python scripts