Hardware Architect, Machine Learning - Google
Sunnyvale, CA
About the Job
Minimum qualifications:
- Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, a related field, or equivalent practical experience.
- 8 years of experience in computer or chip architecture.
- Experience with semiconductor technologies and trends (i.e., including process, memory, interconnect or packaging).
Preferred qualifications:
- Master's degree or PhD in Electrical Engineering, Computer Engineering or Computer Science, with an emphasis on computer architecture, or a related field.
- Experience with deep learning frameworks including TensorFlow and PyTorch.
- Experience with architecting hardware solutions for Machine Learning (ML).
- Experience with outreach to ML researchers and application developers.
- Knowledge of Machine Learning market, technological and business trends, software ecosystem, and emerging applications.
About the job
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.
The US base salary range for this full-time position is $177,000-$266,000 + 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.
Responsibilities
- Create differentiated architectural innovations for Google’s semiconductor TPU roadmap.
- Monitor industrial and academic trends in artificial intelligence and determine where they should intersect our roadmaps.
- Evaluate the power, performance, and cost of prospective architecture and subsystems.
- Engage with system and application software engineers to ensure optimization of the entire hardware/software stack.
- Engage with design, verification, and validation engineers to realize the architecture.
