Physical Design Methodology Lead

Role Overview

We are seeking a senior-level Flow and Methodology Engineer to lead backend design automation for advanced process node SoC and ASIC implementation. This role focuses on enabling robust, scalable, and production-ready RTL-to-GDS flows across synthesis, floorplanning, place and route, clock tree synthesis, static timing analysis, power grid implementation, extraction, physical verification, and signoff closure. The ideal candidate will combine deep technical expertise in physical design methodology with the ability to drive initiatives from concept definition through validation, deployment, and sustained production support.

Key Responsibilities

• Own, develop, and continuously improve backend RTL-to-GDS implementation flows for advanced process nodes, with emphasis on quality of results, scalability, runtime, and designer productivity.
• Drive methodology development across place and route, CTS, timing closure, congestion analysis, ECO implementation, power grid design, IR/EM analysis, extraction, and signoff readiness.
• Partner with design, architecture, DFT, verification, timing, low-power, package, foundry, and signoff teams to ensure flows meet tapeout requirements for complex SoC and IP programs.
• Lead initiatives from concept and feasibility assessment through prototype development, benchmarking, validation, documentation, deployment, training, and production support.
• Establish repeatable best practices, reference flows, automation frameworks, dashboards, and metrics to improve PPA, reduce turnaround time, and improve predictability across projects.
• Work closely with EDA vendors to evaluate, enable, and adopt the latest technologies in physical implementation, signoff, power integrity, machine learning optimization, and agentic/AI-assisted design automation.
• Conduct competitive benchmarking and technology evaluations to identify flow gaps, quantify benefits, and influence vendor roadmaps.
• Debug complex flow, tool, constraint, timing, power, congestion, and physical verification issues, and provide expert guidance to design implementation teams.
• Promote automation-first thinking through scripting, reusable infrastructure, regression systems, data-driven flow analysis, and intelligent decision support.
• Provide deployment support, training, and hands-on technical leadership to ensure new methodologies are adopted effectively across engineering teams.