Drimva

SambaNova

Senior Hardware Design Engineer

$120K–$200K

Austin, Texas, United States +3 · Senior · Full-time · Posted Aug 18, 2026

Location
Austin, Texas, United States
San Jose, California, United States
Austin, Texas, United States
San Jose, California, United States
Experience
Master’s degree: 3–5 years of relevant experience; Bachelor’s degree: 5–7 years.
Education
Master’s degree in Computer Engineering or Electrical Engineering, or Bachelor’s degree.

About the job

Designs and owns RTL blocks for SambaNova’s RDU ASICs, from microarchitecture and implementation through tapeout and silicon bring-up.

What you'll do

  • Author microarchitecture specifications and code, integrate, and debug SystemVerilog RTL for assigned blocks.
  • Meet PPA targets and collaborate with verification and physical design teams on functionality, timing, and area.
  • Run design checks and support post-silicon bring-up and debug; contribute to design-flow improvements, including AI and EDA automation.

What they ask for

  • Master’s degree in Computer Engineering or Electrical Engineering with 3–5 years of relevant experience, or Bachelor’s degree with 5–7 years.
  • Strong SystemVerilog RTL design skills for high-speed, high-performance ASICs; foundation in computer architecture and digital logic design.
  • Experience with PPA tradeoffs, synthesis and STA, lint, CDC, LEC, and SVA.
  • Working knowledge of industry-standard interconnects and protocols; Python and Linux shell scripting; ability to work onsite in San Jose, CA or Austin, TX.

Nice to have

  • Exposure to a full ASIC development cycle through tapeout and production silicon; interest in or knowledge of AI workload acceleration.
  • Low-power design experience at advanced process nodes or exposure to emulation or FPGA prototyping.

Pay

$120,000 — $200,000 USD (base salary)

Tools

SystemVerilog, AXI, PCIe, Ethernet, HBM/DDR, Python, Linux shell, Git

AI focus

Contributes to design-flow improvements, including AI and automation in EDA tool methodologies; works on hardware for AI workload acceleration.

Model-written summary · the original posting is authoritative

First seen Oct 10, 2026 · Last verified Nov 5, 2026 (UTC)