Physical Design Methodology Engineer, AI Hw Ip

Tenstorrent Tenstorrent · Semiconductors · Austin, Belgrade +1 · IP Hardware

Develops, deploys, and owns the RTL-to-GDSII methodology for an AI hardware IP team, focusing on automation, PPA exploration, and integrating AI/ML into design flows for AI accelerators and high-performance CPUs.

What you'd actually do

  1. Develops, deploys, and owns the RTL-to-GDSII methodology the IP physical design team runs on
  2. Building with AI as part of how you develop flows, and opinionated about where LLMs and ML-driven optimization genuinely help versus where they do not.
  3. An effective partner to design teams and EDA vendors, and a clear writer who documents flows well enough that others can run them without you.
  4. An Engineer with 5+ years developing and supporting physical design methodology or CAD flows in production use.
  5. Expertise with industry-standard tools (FusionCompiler/ICC2, Innovus/Genus, PrimeTime, RedHawk) and scripting languages (Tcl, Python, Perl).

Skills

Required

  • Physical design methodology
  • CAD methodology
  • RTL-to-GDSII flows
  • Tcl
  • Python
  • Perl
  • FusionCompiler/ICC2
  • Innovus/Genus
  • PrimeTime
  • RedHawk
  • Advanced node methodology
  • Low-power intent (UPF/CPF)
  • Clock tree synthesis
  • Signoff (EM/IR, DRC/LVS)

Nice to have

  • ML-driven flow optimization
  • Custom CAD development

What the JD emphasized

  • 5+ years developing and supporting physical design methodology or CAD flows in production use
  • Expertise with industry-standard tools (FusionCompiler/ICC2, Innovus/Genus, PrimeTime, RedHawk) and scripting languages (Tcl, Python, Perl).
  • Deep understanding of advanced node methodology, low-power intent (UPF/CPF), clock tree synthesis, and signoff (EM/IR, DRC/LVS).
  • Experience standing up flows for new technology nodes, PDKs, or foundry targets ahead of program need, and qualifying new EDA releases.

Other signals

  • AI accelerators
  • ML-driven flow optimization
  • custom CAD development