Product Engineer, Machine Learning Accelerators

Minimum qualifications:

• Bachelor's degree in Engineering or equivalent practical experience.
• 8 years of experience in manufacturing.
• Experience in design for manufacture and serviceability.
• Experience in PCBA (Printed Circuit Board Assembly) and related system assembly.

Preferred qualifications:

• Master's degree or PhD in Electrical, Mechanical, Industrial, Materials, or a related engineering field.
  
• 10 years of experience in developing supply chains in manufacturing and test.
• Experience with contract manufacturers and suppliers to drive root cause analysis, corrective actions and continuous process improvements.
• Experience working with ODMs, and component suppliers for data center server accelerator products (GPU, FPGA or ASIC).
• Experience of bring-up or bench testing hardware in a lab environment.
• Knowledge of SQL queries and scripting in Python or Bash.

About the job

Be part of a team that pushes boundaries, developing custom silicon solutions that power the future of Google's direct-to-consumer products. You'll contribute to the innovation behind products loved by millions worldwide. Your expertise will shape the next generation of hardware experiences, delivering unparalleled performance, efficiency, and integration.

The Machine Learning Supply Chain and Operations (MLSCO) team is responsible for the deployment of Machine Learning capacity in Google’s Fleet. MLSCO-NPI leads cross-functional program planning and execution to deliver next generation Machine Learning systems from Concept to with operational speed.Behind everything our users see online is the architecture built by the Technical Infrastructure team to keep it running. From developing and maintaining our data centers to building the next generation of Google platforms, we make Google's product portfolio possible. We're proud to be our engineers' engineers and love voiding warranties by taking things apart so we can rebuild them. We keep our networks up and running, ensuring our users have the best and fastest experience possible.

Responsibilities

• Lead the technology assessment for new products. Co-work with the product team to influence design decisions, highlight manufacturing risks, and develop mitigation plans.
• Collaborate with Quality and Reliability Engineers to establish NPI and production goals for yield and reliability. Validate product qualification plans, support reliability testing and review results to ensure product performance meets requirements.
• Lead cross-functional team towards resolution of components and build quality excursions during New Product Introduction build phases.
• Provide on-site and remote support for pre-production builds. Ensure factory readiness, support manufacturing line bring-up, provide product debug training and gather feedback on build issues. Manage and drive yield bridge analysis to improve product quality.