Cloud-Scale Physical Design Engineer for ML Inference

Amazon Web Services (AWS); Austin (TX); On-site; USD 136,000 - 184,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

Paid time off

Parental leave

RSUs

Job summary

Annapurna Labs (U.S.) Inc. in Austin, TX is seeking a hardware-focused engineer to design and optimize data-center hardware using AWS-typical RTL-to-GDS flows.

You will contribute to high-performance, cost-effective machine-learning acceleration hardware and lead improvements across EDA-driven workflows. The role requires deep experience with block design, sign-off activities, and collaboration across RTL, package and DFT teams.

Qualifications

• Bachelor's degree in Electrical Engineering or a related field.
• Block design using EDA tools (Cadence, Mentor Graphics, Synopsys) including synthesis, verification and physical planning.
• Deep understanding of sign-off activities (timing, IR drop, physical verification).

Responsibilities

• Drive block physical implementation through synthesis, floor planning, place and route and sign-off.
• Develop cloud infrastructure to support physical design work.
• Improve RTL2GDS flows for better PPA and turnaround time.
• Create dashboards and reports for project tracking and QoR visualization.
• Interface with RTL, Package Design, DFT and other teams to drive efficiency.
• Collaborate with EDA vendors to evaluate new tools and fixes.

Skills

Block design

EDA tools

RTL optimization

Digital design

Education

Bachelor's degree in Electrical Engineering

Tools

Cadence

Mentor Graphics

Synopsys

Job description

Annapurna Labs (U.S.) Inc. in Austin, TX is seeking a hardware-focused engineer to design and optimize data-center hardware using AWS-typical RTL-to-GDS flows.

You will contribute to high-performance, cost-effective machine-learning acceleration hardware and lead improvements across EDA-driven workflows. The role requires deep experience with block design, sign-off activities, and collaboration across RTL, package and DFT teams.