Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 8 years of experience in product management or related technical role.
• 5 years of experience in ML software platforms or software developer tools, hardware accelerators, operating systems, frameworks, APIs, high-level mobile system architecture.
• 3 years of experience taking technical products from conception to launch.

Preferred qualifications:

• Master's degree or PhD in a technical field.
• 5 years of experience working cross-functionally with engineering, UX/UI, sales finance, and other stakeholders.
• Experience defining and launching software infrastructure products, in the ML or mobile device space.
• Familiarity with compute and software platform development life cycles for ML solutions.
• Excellent communication and people skills to advocate initiatives with key stakeholders and executive management.

About the job

At Google, we put our users first. The world is always changing, so we need Product Managers who are continuously adapting and excited to work on products that affect millions of people every day.

In this role, you will work cross-functionally to guide products from conception to launch by connecting the technical and business worlds. You can break down complex problems into steps that drive product development.

One of the many reasons Google consistently brings innovative, world-changing products to market is because of the collaborative work we do in Product Management. Our team works closely with creative engineers, designers, marketers, etc. to help design and develop technologies that improve access to the world's information. We're responsible for guiding products throughout the execution cycle, focusing specifically on analyzing, positioning, packaging, promoting, and tailoring our solutions to our users.

We are the team that builds Google Tensor - Google's custom System-on-Chip (SoC) that powers Pixel devices. Tensor makes transformative user experiences possible with the help of cutting-edge ML running on-device. Our goal is to productize the latest ML innovations and research by delivering computing hardware and software. In this role, you will drive our strategy across the large number of software frameworks relevant for running ML on-device across CPU, GPU, and TPU.

Google's mission is to organize the world's information and make it universally accessible and useful. Our Devices & Services team combines the best of Google AI, Software, and Hardware to create radically helpful experiences for users. We research, design, and develop new technologies and hardware to make our user's interaction with computing faster, seamless, and more powerful. Whether finding new ways to capture and sense the world around us, advancing form factors, or improving interaction methods, the Devices & Services team is making people's lives better through technology.

The US base salary range for this full-time position is $168,000-$252,000 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 our GPU software vision and strategy, determining investment areas across key frameworks for internal and external use cases.
• Guide our CPU software vision and strategy, identifying ML workloads to optimize for CPU and when to leverage existing solutions.
• Collaborate closely with our TPU SDK team on strategy for Python ML frameworks and their mobile variants.
• Develop comprehensive benchmarks for ML inference and compute-intensive workloads, both internally and externally.
• Define the product strategy for our next-gen heterogeneous runtime, aiming to fully leverage CPU GPU TPU DSP and potentially set industry standards, while influencing future hardware direction to position Google Tensor as a premium SoC.