Staff Silicon Architect: AI Accelerators & SoC Design

Google DeepMind; Mountain View (CA); On-site; USD 256,000 - 278,000; Full time

Job summary

Google DeepMind is seeking a Senior hardware architect to design and implement accelerators for codesigned systems. You will own subsystems from requirements through tapeout and bringup, collaborating with application and performance teams to optimize efficiency and programmability.

The role spans hardware architecture, logic design, design verification, and physical design with ML model design and numerics. Google DeepMind values safety and ethics in AI development.

Qualifications

• Bachelor's degree or equivalent practical experience in Electrical/Computer Engineering or CS.
• 10 years of experience leading multiple design domains (Design For Test, Design Verification, Physical Design).
• 5 years of experience with logic design, computer architecture, and circuit theory.
• 5 years of experience in power or performance modeling or system performance analysis.

Responsibilities

• Architect, design, and implement hardware for novel systolic array accelerators.
• Own subsystems up to the entire accelerator, from requirements through tapeout and bringup.
• Collaborate with experts to codesign solutions for efficiency, performance and programmability.
• Write code for accelerators to meet production requirements at system, library, and application levels.
• Bring a generalist approach to computer systems across disciplines.

Skills

Design leadership

Hardware design

Verification

Physical design

Power/performance modeling

Processor architectures

Education

Bachelor's degree in Electrical Engineering / Computer Engineering / Computer Science

Master's or PhD in Electrical Engineering, Computer Engineering or Computer Science

Tools

Linux kernel development

Processor core architectures (ARM, x86, RISC-V)

Job description

Google DeepMind is seeking a Senior hardware architect to design and implement accelerators for codesigned systems. You will own subsystems from requirements through tapeout and bringup, collaborating with application and performance teams to optimize efficiency and programmability.

The role spans hardware architecture, logic design, design verification, and physical design with ML model design and numerics. Google DeepMind values safety and ethics in AI development.