Physical Design CPU Methodology Engineer

Tenstorrent is leading the industry on cutting-edge AI technology, revolutionizing performance expectations, ease of use, and cost efficiency.

With AI redefining the computing paradigm, solutions must evolve to unify innovations in software models, compilers, platforms, networking, and semiconductors.

Our diverse team of technologists have developed a high performance RISC-V CPU from scratch, and share a passion for AI and a deep desire to build the best AI platform possible.

We value collaboration, curiosity, and a commitment to solving hard problems.

We are growing our team and looking for contributors of all seniorities.

• You are an experienced Physical Design Engineer with 10+ years of ASIC/SoC/CPU experience, ideally focused on high-performance CPUs or similarly complex digital designs.
• You bring deep expertise across RTL-to-GDS implementation and signoff, including timing closure, power optimization, SI, EM/IR, extraction, and physical verification.
• You have built or owned physical design methodology, automation, or CAD flows and are highly skilled in Tcl, Python, Perl, or similar scripting languages.
• You are a technical leader who can influence across teams, mentor engineers, and drive standards across geographically distributed organizations.
• Define and own scalable CPU physical design methodology spanning synthesis, floorplanning, power planning, place and route, CTS, extraction, STA, SI, EM/IR, and physical verification.
• Explore AI/ML-enabled PD tooling, begin synthesis and PnR of CPU partitions, and start developing the new program RM flow.
• Drive automation, constraints, quality metrics, signoff criteria, dashboards, checkers, and debug workflows that improve predictability, turnaround time, and PPA.
• Partner with architecture, RTL, circuit, DFT, CAD, program, data, and EDA vendor teams to enable advanced nodes and productize AI/ML across physical design.
• How to apply AI/ML to physical design for hotspot prediction, congestion forecasting, timing-risk classification, ECO prioritization, IR-drop screening, and flow tuning.
• How to build reliable, explainable, production-ready data infrastructure, including feature collection, validation, and signoff integration.
• How to evaluate AI-enabled EDA capabilities and determine where data-driven optimization delivers measurable value over conventional heuristics.