Product Engineer, Machine Learning and GPU Accelerators

Minimum qualifications:

• Bachelor's degree in Electrical Engineering, or in a related technical field, or equivalent practical experience.
• 8 years of experience at a company developing supply chains in manufacturing and testing.
• Experience working with Original Device Manufacturers (ODMs), contract manufacturers and component suppliers for electronics assembly.

Preferred qualifications:

• Master's degree in Electrical Engineering, or a related field.
• 10 years of experience at a company developing supply chains in manufacturing and testing.
• Experience working with ODMs, contract manufacturers and component suppliers for data center server accelerator products e.g., Graphics Processing Unit (GPU), Field-programmable Gate Array (FPGA), or Application-Specific Integrated Circuit (ASIC).
• Experience working with contract manufacturers and suppliers to drive root cause analysis, corrective actions, and continuous process improvements.
• Experience with bring-up or bench testing hardware in a lab environment.
• Knowledge of SQL queries and scripting in Python or Bash.

The Machine Learning Supply Chain and Operations (MLSCO) team is responsible for the deployment of Machine Learning capacity in Google’s Fleet. MLSCO and New Product Introduction (NPI) leads cross-functional, program planning and execution to deliver next generation Machine Learning systems from Concept to End of Life (EOL), with operational excellence and 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.

• Lead reviews between Design and Operations Engineering to drive deliverables that are key to implementing the manufacturing plan, such as Design for Excellence (DFx) and test strategy.
• 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 the bonepile and drive yield bridge analysis.
• 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 targets for yield and long term reliability. Validate product qualification plans, support reliability testing and review results.
• Lead cross-functional team towards resolution of component/build quality excursions. Support second source part qualification and coordinate Bill of Materials (BOM) updates.