Product Engineer, Machine Learning and GPU Accelerators
Listed on 2026-06-18
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Engineering
Manufacturing Engineer, Systems Engineer, Electrical Engineering, Quality Engineering
Minimum Qualifications
- Bachelor's degree in Electrical Engineering, a related field, or equivalent practical experience.
- 8 years of experience as a Manufacturing, Quality, Reliability, or Product Engineer.
- 5 years of experience with a company developing supply chains in manufacturing and testing.
- Experience working with Original Device Manufacturers (ODMs), contract manufacturers and component suppliers for data center class products.
- Master's degree in Electrical Engineering, or a related field.
- 10 years relevant work experience at a company developing supply chains in manufacturing and testing.
- Experience working with Original Device Manufacturers (ODMs), contract manufacturers and component suppliers for data center server accelerator products (GPU, FPGA or ASIC based).
- History of working with contract manufacturers and suppliers to drive root cause analysis, corrective actions and continuous process improvements.
- Experience of bring-up and/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 New Product Introduction (NPI) leads cross‑functional program planning and execution to deliver next‑generation Machine Learning systems from Concept to EOL. Together, we are building the engine which powers Google's ML capability and driving the evolution of artificial intelligence.
The US base salary range for this full‑time position is $134,000–$198,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job‑related skills, experience, and relevant education or training.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits.
- Lead reviews between Design and Operations Engineering to drive deliverables that are key to implementing the manufacturing plan, such as 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 to improve product quality.
- 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. Validate product qualification plans, support reliability testing and ensure product performance meets requirements.
- Lead cross‑functional team towards resolution of component/build quality excursions during NPI build phases.
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