Senior Product Engineer, Machine Learning Accelerators
Content + Source + Freshness • 14 Dec 2025 • 95% confidence
Offer value
This position provides a pathway into impactful product engineering with competitive compensation, making it valuable for experienced candidates in manufacturing and hardware engineering.
- Competitive salary: $138,000–$202,000/year
- Work on advancing machine learning technologies
- Opportunities for cross-functional collaboration and growth
Pros
- Attractive salary range ($138,000 - $202,000) for the role
- Opportunity to work on cutting-edge machine learning hardware solutions
- Collaborative environment aiding professional growth
Cons
- Experience in manufacturing is crucial, limiting candidate pool
- May involve complex problem-solving under pressure
- Requires collaboration with various technical teams, impacting independent work
Who it's for
Senior / Mid-level • Onsite
Good fit
- Experienced product engineers in manufacturing environments
- Individuals with a passion for hardware and machine learning
- Professionals looking to innovate products in engineering
Not recommended for
- Entry-level candidates or those without engineering experience
- Individuals seeking fully remote roles
- Professionals uncomfortable with teamwork-related tasks
Motivation fit
Key skills
About the job
Senior Product Engineer, Machine Learning Accelerators
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Mid
Minimum qualifications:
- Bachelor's degree in Engineering or equivalent practical experience.
- 8 years of experience in manufacturing.
- Experience in PCBA (Printed Circuit Board Assembly) and related system assembly.
- Experience in design for manufacturability and serviceability.
Preferred qualifications:
- Master's degree or PhD in Electrical, Mechanical, Industrial, Materials, or a related engineering field.
- 10 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 data center server accelerator products (GPU, FPGA or ASIC).
- Experience working with contract manufacturers and suppliers to drive root cause analysis, corrective actions and continuous process improvements.
- 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
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 End of Life (EOL), with operational excellence and speed. Together, we are building the engine which powers Google's Machine Learning capability and driving the evolution of artificial intelligence. In the Product Engineering team, we're proud to be our engineers' and love the thrill of solving complex problems to bring designs to life and make advanced technology work at a massive scale.
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 New Product Introduction (NPI) and production goals for yield and long term 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 the bonepile and drive yield bridge analysis to improve product quality.
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