About the job

We are the team that builds Google Tensor - Google’s first custom System-on-Chip (SoC) that powers the latest Pixel phones. Tensor makes transformative user experiences possible with the help of cutting-edge Machine Learning (ML) running on Tensor TPU. Our goal is to productize the latest ML innovations and research by delivering computing hardware and software.

Google's mission is to organize the world's information and make it universally accessible and useful. Our team combines the best of Google AI, Software, and Hardware to create radically helpful experiences. We research, design, and develop new technologies and hardware to make computing faster, seamless, and more powerful. We aim to make people's lives better through technology.

The US base salary range for this full-time position is $208,000-$306,000 + 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 for new hire 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. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

• Lead a team of product managers.
  
• Lead a team through defining, identifying, collecting, and tracking appropriate product or business metrics.
  
• Drive TPU roadmap for power, performance, and area tradeoffs support for workload interactions with the rest of the system on chip compute elements.
  
• Work cross-functionally to assess and select challenges to overcome through custom silicon, build multi-year roadmaps that intersect our long-term device strategy, and secure executive support for new ML initiatives.
  
• Ensure our on-device ML has a clear path to productization, and drive internal stakeholder and partner engagements necessary for the on-time launch of the product.