Physical Design Flow & Methodology Engineer

TLDR

Drive PPA optimization and build scalable reference flows for integrating Quadric GPNPU soft IP while providing hands-on support during the customer tapeout process.

Quadric delivers its GPNPU as soft IP — RTL and implementation collateral — enabling customers to integrate our processor into their own SoCs across a range of process nodes and foundries. You will drive PPA optimization across IP configurations, build the scalable reference flows customers use to evaluate and integrate our IP, and provide hands-on implementation support to customers working toward their tapeouts.

Responsibilities

PPA Optimization & Analysis

  • Drive PPA analysis and optimization for Quadric GPNPU soft IP across process nodes and hardware configurations — timing, area, leakage, and dynamic power
  • Apply low-power techniques (clock gating, multi-Vt, operand isolation) and synthesis/P&R knobs to hit frequency and area targets
  • Characterize the IP design space across configurations and build PPA models that support customer evaluations and pre-sales engagements
  • Partner with RTL and architecture teams early to quantify tradeoffs and influence design decisions before they become costly to reverse

Reference Flow Development

  • Build and maintain a scalable RTL-to-GDS reference flow for Quadric soft IP that customers can use to evaluate, integrate, and close PPA in their own SoC environment
  • Ensure the flow is portable across supported process nodes with clear BKMs, SDC templates, floorplan scripts, and integration guidelines
  • Develop TCL and Python automation — and leverage AI coding tools such as Claude — to accelerate flow development, reduce manual effort, and improve repeatability
  • Qualify EDA tool updates and benchmark QoR impact before rolling into the reference flow

Customer Integration & Tapeout Support

  • Act as the primary PD contact for customers integrating Quadric soft IP, guiding them from evaluation through their SoC tapeout
  • Help customers adapt the reference flow to their process node, foundry PDK, and internal design environment
  • Triage and resolve customer-reported implementation issues — timing, congestion, power, or flow failures — working with internal teams to deliver fixes or updated collateral
  • Support FAE and business development with PPA feasibility studies for new customer engagements

Collaboration & Documentation

  • Work with architecture, RTL, and software teams to ensure IP deliverables meet customer-facing PPA targets
  • Document methodologies, BKMs, and optimization learnings; maintain process node bring-up guidelines to support IP portability

Requirements

Education & Experience

  • BS/MS in Electrical Engineering, Computer Engineering, or related field
  • 4+ years of ASIC or processor IP physical design experience focused on PPA optimization and flow development across advanced nodes

Technical Skills

  • Proficiency with industry-standard physical design tools from Synopsys or Cadence (synthesis, place-and-route, and timing analysis)
  • Experience with advanced FinFET process nodes (16nm and below); multi-node experience preferred
  • Strong TCL scripting and Python automation skills
  • Solid understanding of synthesis and P&R levers for PPA — timing paths, cell selection, congestion, and power intent
  • Hands-on experience with low-power design techniques and MCMM timing analysis
  • Comfort using AI tools (e.g., Claude, Copilot) to accelerate script development, automate repetitive EDA tasks, and improve workflow productivity
  • Understanding of DFT concepts (scan, ICG bypass) and their physical design implications

Nice to Have

  • Experience delivering soft IP to external customers or supporting SoC integrators through tapeout
  • Background in AI accelerator, NPU, or DSP processor IP implementation
  • Exposure to metrics-driven QoR tracking and large-scale synthesis run management

Quadric builds a groundbreaking general purpose neural processing unit (GPNPU) architecture that enhances the performance of neural network inference on edge devices. Our technology caters to developers and businesses looking to leverage AI in real-time applications, distinguishing itself by combining supercomputing capabilities with the immediacy required by the edge.

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