Machine Learning Acceleration Silicon Architect - Google
Mountain View, CA
About the Job
Minimum qualifications:
- Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, a related field, or equivalent practical experience.
- 5 years of experience in architecture or silicon engineering, particularly with DL accelerators, GPUs, DSPs, VLSI, and RTL.
- Experience with machine learning and deep learning principles.
Preferred qualifications:
- Master's degree or PhD in Electrical Engineering, Computer Engineering or Computer Science, with an emphasis on computer architecture.
- Experience with workload analysis and performance modeling for DL accelerators.
- Experience in design space explorations and solution/optimization delivery for DL accelerators.
- Experience in thermal/power analysis of DL accelerators.
- Understanding of DL hardware architecture and computer hardware architecture design.
About the job
In this role, you will develop system architectures with hardware acceleration for deep learning use cases. You will work with research, algorithm, product managers, hardware/software/soc architecture, and implementation teams to define the end-to-end ML acceleration solution. The solution space will include custom hardware and software, and the solution will have the entire technology stack considered. Overall, you will play a critical role in enabling Google-only on-device experiences to the users.
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 $150,000-$223,000 + 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
- Analyze critical ML workloads and pinpoint hardware acceleration opportunities.
- Design groundbreaking architectures to achieve exceptional deep learning performance and efficiency.
- Investigate how evolving hardware and software architectures will shape the algorithms, programming models, and applications of tomorrow.
- Collaborate closely with algorithm and research teams to create hardware-optimized networks.
