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Physical Design Engineer: Die-to-Die Interface (RTL to GDSII)

Tenstorrent · Santa Clara, California, United States · Santa Clara

CHECKED 22H AGO

Infrastructureonsite$100k–$500k
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At a glance

Highlights

  • Drive die-to-die physical implementation from RTL to GDSII for AI hardware.
  • Close high-speed die-to-die interfaces for multi-die chiplet architectures.
  • Take high-speed D2D PHYs and controllers from netlist to tapeout.

What they offer

  • Base and variable compensation targets
  • Highly competitive compensation package and benefits

Ideal candidate

A seasoned, detail-oriented problem solver who thrives on hard closure challenges and collaborating across analog, digital and full-chip teams to deliver high-speed multi-die designs.

Requirements

  • 5+ years ASIC physical design experience at advanced nodes (7nm or below) with multiple successful tapeouts
  • Own blocks from RTL to GDSII across synthesis, floorplanning, place-and-route, CTS and sign-off
  • Experience closing high-speed interfaces such as D2D, PCIe, HBM and SerDes including timing, signal and power integrity
  • Proficient in STA, constraints and closure for complex high-speed designs
  • Experience with EDA tools from Synopsys, Cadence and Mentor for implementation and verification
  • Experience with Tcl and Python for flow automation and methodology improvements
  • B.S./M.S. in EE, CE or related field
remote policy
Remote - open to any location in the U.S.
hiring process
Candidates will be assessed for the appropriate level during the interview process.

Lab profile

About Tenstorrent

Builds high-performance RISC-V CPUs and AI accelerators with full-stack software for efficient AI computing.

What they build

  • AI accelerators
  • RISC-V CPUs
  • AI software and compilers

Known for

  • Custom RISC-V CPU development from scratch
  • Full-stack AI hardware and software
  • Performance, ease of use and cost efficiency

Key facts

  • Founded in 2016
  • Headquartered in Toronto, Canada
  • Backed by Samsung and Hyundai Motor Group
More about Tenstorrent →
Apply on the lab’s site ↗