Amazon ASIC Design Engineer Cloud-Scale Machine Learning Acceleration team 

United States, California, Cupertino 

449867103

Custom SoCs (System on Chip) live at the heart of AWS Machine Learning servers. As a member of the Cloud-Scale Machine Learning Acceleration team you’ll be responsible for the design and optimization of hardware in our data centers including AWS Inferentia, our custom designed machine learning inference datacenter server. Our success depends on our world-class server infrastructure; we’re handling massive scale and rapid integration of emergent technologies. We’re looking for an ASIC Design Eengineer to help us trail-blaze new technologies and architectures, while ensuring high design quality and making the right trade-offs.
Key job responsibilities
As an ASIC Design Engineer, you will:• Develop and implement high-performance, area and power-efficient RTL designs to meet project specifications and targets• Conduct in-depth analysis of designs, microarchitectures, and architectures to optimize trade-offs between features, power consumption, performance, and area requirements• Create microarchitectures, implement SystemVerilog RTL, and deliver synthesis and timing-clean designs with appropriate constraints• Execute lint and clock domain crossing quality checks to ensure design integrity• Collaborate closely with cross-functional teams, including architects, fellow designers, verification specialists, pre- and post-silicon validation teams, and synthesis, timing, and back-end experts
You will thrive in this role if you:
- Have a "Learn and Be Curious" mindset
- Have familiarity with key components such as interconnects, DMAs, Memory sub-systems, accelerator engines, debug and system level architectures
Work/Life Balance
Mentorship & Career Growth

- Bachelor's degree or equivalent
- 3+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience

- Master's degree in computer science or equivalent
- 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience