AWS Utility Computing (UC) is seeking a Physical Design Engineer specializing in Static Timing Analysis to join their Cloud-Scale Machine Learning Acceleration team. This role is part of Annapurna Labs, focusing on developing cutting-edge hardware solutions like AWS Inferentia for machine learning inference.
The position involves working on critical timing analysis for complex hardware designs that power AWS's cloud infrastructure. You'll be responsible for developing and maintaining timing analysis flows, providing crucial feedback to team members, and ensuring the performance and reliability of AWS's machine learning acceleration hardware.
As a senior member of the team, you'll collaborate with various engineering groups, from RTL designers to architecture teams, contributing to the development of next-generation cloud computing hardware. The role combines deep technical expertise in timing analysis with the excitement of working on large-scale deployments that impact millions of AWS customers.
The ideal candidate brings strong experience in static timing analysis, constraint development, and physical design, along with programming skills in languages like Python or Tcl. You'll work in a fast-paced, start-up-like environment within Amazon's larger organization, where innovation and high standards are core values.
This position offers competitive compensation, comprehensive benefits, and the opportunity to work on cutting-edge technology that shapes the future of cloud computing and machine learning acceleration. You'll be part of AWS's inclusive culture that values diverse perspectives and promotes continuous learning and growth.
Working at AWS means joining a team that's pioneering cloud computing technology and consistently pushing the boundaries of what's possible. Whether you're interested in technical challenges, career growth, or making a significant impact on global cloud infrastructure, this role offers the opportunity to achieve all three while working with industry-leading experts in hardware design and cloud computing.