Systems Yield Engineer, Quantum AI

As a part of the Quantum Systems Yield Engineer, you will drive development and execution of comprehensive testing to characterize the yield and reliability of critical components and assemblies. In this role, you will develop innovative test methods and custom fixtures for both room temperature and cryogenic environments, collaborate closely with software and hardware teams, ensure that test and yield data remains traceable and accessible to facilitate rapid decision-making. You will partner with multiple QAI teams to keep our test coverage up to date with rapid hardware advancements.The full potential of quantum computing will be unlocked with a large-scale computer capable of complex, error-corrected computations. Google Quantum AI's mission is to build this computer and unlock solutions to classically intractable problems. Our roadmap is focused on advancing the capabilities of quantum computing and enabling meaningful applications.Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $132000 - $189000 (USD) + 15% bonus target + equity + benefits Learn more about benefits at Google .

Specify, design, and maintain mechanical, electrical, and thermal test fixturing for component, sub-assembly, and system testing. Develop new test methods for both room temperature and cryogenic environments. Partner with software and hardware teams to assure test and yield data are traceable and accessible for fast decision-making. Perform data analysis in support of new tests and root cause analysis. Work with internal hardware teams and vendors to create pass/fail criteria and automation for test analysis.

Minimum qualifications: Bachelor’s degree in Electrical Engineering, Computer Engineering, Computer Science, Physics, or a specialized field (e.g., Optics, Sensors, Audio/DSP, etc.), or equivalent practical experience. 3 years of experience with electrical testing. 3 years of experience using statistical and data analysis tools. Preferred qualifications: Master's degree or PhD in Electrical Engineering, Computer Engineering, Physics, or a related field (e.g., Optics, Sensors, Audio/DSP). Experience with data visualization libraries like Plotly or matplotlib and experience contributing to and collaborating within a shared codebase. Experience with cryogenic testing. Familiarity with PCB design principles.and Understanding of mechanical design fundamentals. Familiar with understanding electrical noise and measurement sensitivity. Proficiency in data analysis using tools such as Python, JMP, ReliaSoft, or similar.