Silicon Architecture/Design Engineer, PhD, Early Career
Description For Silicon Architecture/Design Engineer, PhD, Early Career
Google is seeking a Silicon Architecture/Design Engineer to join their ML, Systems, & Cloud AI (MSCA) organization. This role focuses on shaping the future of AI/ML hardware acceleration through work on TPU (Tensor Processing Unit) technology. The position combines hardware architecture, machine learning, and system design expertise to develop next-generation AI accelerators.
The successful candidate will work at the intersection of hardware and AI, developing architectural specifications, performance models, and hardware/software interfaces for Google's TPU platforms. This role involves close collaboration with hardware design, software, compiler, and ML research teams to optimize AI/ML workloads and implement efficient hardware solutions.
Key responsibilities include characterizing ML workloads, developing architecture specifications, creating performance models, and implementing verification strategies. The role requires expertise in computer architecture, digital design, and programming, with a focus on high-performance and low-power design techniques.
This position offers the opportunity to work on cutting-edge AI hardware at one of the world's leading technology companies. The work directly impacts Google's most demanding AI/ML applications and services, including Search, YouTube, and Google Cloud. The role combines technical depth in hardware design with the broad impact of enabling next-generation AI capabilities.
Google offers a collaborative environment, working with world-class engineers and researchers. The position provides exposure to the latest developments in AI/ML hardware acceleration and the chance to influence the future of AI computing infrastructure. This is an excellent opportunity for someone passionate about hardware architecture and AI technology to make a significant impact on the field.
Responsibilities For Silicon Architecture/Design Engineer, PhD, Early Career
- Revolutionize Machine Learning (ML) workload characterization and benchmarking, and propose capabilities and optimizations for next-generation TPUs
- Develop architecture specifications that meet current and future computing requirements for AI/ML roadmap
- Partner with hardware design, software, compiler, Machine Learning (ML) model and research teams for effective hardware/software codesign
- Develop and adopt advanced AI/ML capabilities, drive accelerated and efficient design verification strategies
- Use AI techniques for faster and optimal Physical Design Convergence
Requirements For Silicon Architecture/Design Engineer, PhD, Early Career
- PhD degree in Electronics and Communication Engineering, Electrical Engineering, Computer Engineering or related technical field, or equivalent practical experience
- Experience with accelerator architectures and data center workloads
- Experience in programming languages (e.g., C++, Python, Verilog), Synopsys, Cadence tools
- 2 years of experience post PhD (preferred)
- Experience with performance modeling tools (preferred)
- Knowledge of arithmetic units, bus architectures, accelerators, or memory hierarchies (preferred)
- Knowledge of high performance and low power design techniques (preferred)
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