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Lead Graphics Processing Unit Architect
Job Summary
Job Description
- 10+ years GPU, graphic architecture, silicon design experience
- Bachelor's degree in relevant field
- Lead GPU architecture development
- Collaborate cross-functionally
- Define hardware IP architecture specifications
- Perform detailed data analysis and tradeoff evaluations
- Master's or PhD in Electrical Engineering, Computer Engineering, or Computer Science
- Experience with low-power GPU hardware IP for SoCs
- Experience in microarchitecture, power, and performance optimization
- Experience with Machine Learning (ML), GPU Software, Android APIs, and devices
- Bonus
- Equity
- Benefits
Job Details
Minimum qualifications:
- Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, or a related technical field, or equivalent practical experience.
- 10 years of experience in Graphics Processing Unit (GPU), graphic architecture and silicon design.
Preferred qualifications:
- Master's degree or PhD in Electrical Engineering, Computer Engineering, or Computer Science.
- Experience collaborating cross-functionally with Product Management, SoC architecture, IP design and verification, Algorithm and Software Development teams.
- Experience architecting and designing low-power GPU hardware Internet Protocol (IP) for System on a Chips (SoC).
- Experience in micro architecture, power and performance optimization.
- Experience with Machine Learning (ML), GPU Software, Android first-party (1P)/third-party (3P) Application Programming Interface (API) and Devices.
About the job
Be part of a diverse team that pushes boundaries, developing custom silicon solutions that power the future of Google's direct-to-consumer products. You'll contribute to the innovation behind products loved by millions worldwide. Your expertise will shape the next generation of hardware experiences, delivering unparalleled performance, efficiency, and integration.
In this role, you will contribute to architecture by providing hardware solutions for Graphics Processing Unit (GPU). You will collaborate with algorithm, power and performance teams in Power Performance Area (PPA) optimizations and develop the architecture specifications used by the hardware Internet Protocol (IP) design teams to implement the solutions within the System on a Chip (SoC). You will work cross-functionally with many teams across Google to define competitive and key differentiating user experiences on Google hardware devices and drive these user experiences into Google silicon.
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 $221,000-$314,000 + 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. 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 .
Responsibilities
- Lead Graphics Processing Unit (GPU) architecture and development for the next-generation of Tensor System on a Chip (SoC) to boost performance and power based on Graphics, Machine Learning (ML) and User Interface (UI) workload analysis.
- Collaborate with Google research and silicon product management to help define the product roadmap for GPU hardware IP on SoC for a variety of Google devices.
- Propose architectural features/requirements for GPU to better integrate GPU with Tensor SoC to improve overall performance and optimize the overall Tensor SoC and software stack for GPU workloads.
- Define and deliver the hardware IP architecture specifications that meet competitive power, performance, area, and image quality goals, which will require owning goals through to tape-out and product launch.
- Perform detailed data analysis and tradeoff evaluations to improve GPU architecture solutions.
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