Silicon AI/ML Architect, TPU, Google Cloud
Job Summary
Job Description
- 10+ years in ML/graphics SoC architecture
- 8+ years in multi-chiplet SoC tradeoff analysis
- ASIC design flow experience (spec to production)
- SoC architecture development & specification
- High-performance hardware/software interface creation
- SoC design, RTL coding, integration
- Performance estimation, test plan definition
- C++, Python, or Go programming
- High-performance/low-power design knowledge
- Knowledge of arithmetic units, bus architectures, accelerators, or memory hierarchies
2 skills required
Job Details
Minimum qualifications:
- Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, a related field, or equivalent practical experience.
- 10 years of experience in architecture of machine learning or graphics SoCs.
- 8 years of experience with performance, power, area, cost tradeoff analysis for multi chiplet SoC architectures.
- Experience with ASIC design flows, from specification to production.
Preferred qualifications:
- Experience in SoC designs, RTL coding and integration flows.
- Experience working with software teams optimizing the hardware/software interface.
- Experience in estimating performance by analysis and modeling, and experience in defining and driving performance test plans.
- Experience in a procedural programming language (e.g., C++, Python, Go).
- Knowledge of high performance and low power design techniques.
- Knowledge of arithmetic units, bus architectures, accelerators, or memory hierarchies.
About the job
As a Silicon AI/ML Architect, you will drive the Architecture of an AI/ML SoC and will support the engineering teams during the implementation and validation phases, pre-silicon, and post-silicon. You will work with internal system teams and external partners to develop an understanding of the required SoC functions given its surrounding hardware, software, and the economic environments. You will also collaborate with the Engineering teams to drive the optimal balance between performance, features, power, schedule, and cost.
The ML, Systems, & Cloud AI (MSCA) organization at Google designs, implements, and manages the hardware, software, machine learning, and systems infrastructure for all Google services (Search, YouTube, etc.) and Google Cloud. Our end users are Googlers, Cloud customers and the billions of people who use Google services around the world.
We prioritize security, efficiency, and reliability across everything we do - from developing our latest TPUs to running a global network, while driving towards shaping the future of hyperscale computing. Our global impact spans software and hardware, including Google Cloud’s Vertex AI, the leading AI platform for bringing Gemini models to enterprise customers.
Responsibilities
- Develop SoC architecture and Architecture specifications that meet current and future computing requirements for AI/ML roadmap.
- Evaluate different silicon solutions for executing Google’s data center AI accelerator roadmap: off-the-shelf components, vendor co-developments, custom designs, and chiplets.
- Collaborate with Systems and Software teams to create high performance hardware/software interfaces.
- Collaborate with design, verification, emulation, physical design, packaging, and silicon validation stakeholders to ensure that designs are complete, correct, and performant.
- Evaluate power, performance, area tradeoffs and drive improvements across generations.
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About The Company
A problem isn't truly solved until it's solved for all. Googlers build products that help create opportunities for everyone, whether down the street or across the globe. Bring your insight, imagination and a healthy disregard for the impossible. Bring everything that makes you unique. Together, we can build for everyone.
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