Tenstorrent

Physical Design Engineer, AI Silicon Design (Austin, Santa Clara or Toronto)

Full-Time in Austin, Santa Clara or Toronto, TX - Senior

Tenstorrent is helping enable a new era in artificial intelligence (AI) and deep learning with its breakthrough processor architecture and software. The company’s mission is to deliver orders of magnitude better performance and efficiency for AI workloads from the datacenter to edge of Cloud by co-designing hardware, software and AI algorithms with our unique technology.

Tenstorrent’s architecture scales from datacenter servers to IoT devices with dramatically improved efficiency, flexibility, programmability compared to legacy accelerators including CPUs, GPUs, FPGAs, and TPU-type processors. It is developed by our world-class team with deep expertise in computer architecture, hardware design and verification, systems engineering, compilers, software development, and machine learning algorithms.

Our engineering-based culture is focused on achieving the highest levels of AI innovation across all of Tenstorrent’s technical disciplines. We constantly strive to blend best-in-class aspects of integrity, openness, diversity and collaboration throughout the company: from the CEO to the engineering leadership and to the newest employee who may be a recent college graduate. By joining Tenstorrent, you will be an integral part of a highly accomplished and distinguished team that has many years of experience at companies that include AMD, Arm, Intel and NVIDIA, and that thrives on delivering new, innovative products.

Based in Toronto with operations in Austin and California, Tenstorrent is growing quickly. And, we are proudly backed by top-tier Venture Capital firms including Real Ventures and Eclipse Venture Capital, as well as prominent industry luminaries.

Tenstorrent offers a highly competitive compensation package and benefits, and we are an equal opportunity employer

AI Silicon Design – Physical Design Engineer

At Tenstorrent, we are trailblazing industry’s first dynamic artificial intelligence architecture. We are building best-in-class silicon and software stack for AI/ML workloads.

Role:

Physical design for high-performance designs going into industry leading AI/ML architecture. The person coming into this role will be involved in all implementation aspects from synthesis to tapeout for various IPs on the chip. The work is done alongside with a group of highly experienced engineers across various domains of the AI chip.

Responsibilities:

  • Define PD requirements by working closely with the front-end team, understand the chip architecture and drive physical aspects early in the design cycle
  • Physical design tasks including such as synthesis, PnR, timing closure, area improvement, floorplanning, clocking, I/O planning and power optimization
  • Discussions with 3rd party IP providers, foundry partners and design services
  • End to end tasks from flow development to sign-off
  • Deploy innovative techniques for improving power, performance and area of the design, drive experiments with RTL, and evaluate synthesis, timing and power results

Experience and qualifications:

  • BS/MS/PhD in EE/ECE/CE/CS with at least 5 years of industry experience
  • Hands-on experience with synthesis, block and chip level implementation with industry standard PnR flows and tools
  • Strong experience in SOC/ASIC/GPU/CPU design flows on taped out designs, expertise in timing closure at block/chip levels and ECO flows
  • Experience with back-end design tools such as Primetime, Innovus, RedHawk, etc.
  • Knowledge of low-power design flows such as power gating, multi-Vt and voltage scaling
  • Strong programming skills in Tcl/Perl/Shell/Python
  • Excellent understanding of logic design fundamentals and gate/transistor level implementation
  • Exposure to DFT is an asset
  • Prior experience working on high performance technology nodes and understanding of deep sub-micron design problems/solutions
  • Strong problem solving and debug skills across various levels of design hierarchies

Location

  • Multiple geographies: Santa Clara, Austin, Toronto