We are seeking a seasoned Sr Staff Physical Design Engineer to provide technical leadership across full-chip and multi-die physical implementation on advanced TSMC FinFET nodes. You will define and own the backend design methodology, champion Agentic AI integration into the PD flow, and mentor engineers across the team while interfacing directly with foundry, EDA vendors, and executive stakeholders.
What You'll Do
Architect and own the full-chip physical design strategy: floorplanning, power delivery network (PDN), clock architecture, PnR, and timing closure
Define and drive backend methodology standards across the team for advanced node tapeouts (N5/N4P/N3)
Lead critical timing closure efforts - MCMM, OCV, advanced ECO strategies, and cross-corner sign-off
Oversee and guide signal integrity, power integrity, EM/IR, and reliability analysis for full-chip designs
Architect and lead the development of scalable PD flow infrastructure using Python and TCL - including automated regression, sign-off reporting, and run management systems
Champion the adoption of Agentic AI in the design flow - define use cases, evaluate tools, prototype autonomous agents for floorplan exploration, timing closure, and ECO automation
Lead cross-functional design reviews with DFT, verification, analog, and process engineering teams
Interface with TSMC, EDA vendors (Synopsys, Cadence, Siemens), and internal research teams to evaluate and adopt new methodologies
Mentor and technically guide junior and mid-level PD engineers across the organization
Requirements: 10+ years of experience in digital Physical Design with a proven track record of tapeouts on FinFET nodes (N7 and below)
Deep expertise in full-chip and block-level floorplanning, clock architecture, STA, PnR, and physical sign-off
Expert proficiency in EDA tools: Synopsys ICC2/Fusion, Cadence Innovus, Calibre, PrimeTime, RedHawk / Voltus, StarRC / Quantus
Advanced Python and TCL scripting - ability to architect large-scale PD automation frameworks and flow infrastructure
Proven experience in flow development: designing, implementing, and owning end-to-end backend design flows at team or organization level
Demonstrated experience or vision for applying Agentic AI methodologies to EDA challenges - autonomous agents, AI-driven optimization, LLM-assisted debugging
Strong communication skills - ability to present methodology decisions, risk assessments, and tapeout readiness to senior management
Experience with power optimization (DVFS, multi-Vt, power domains, UPF/CPF)
This position is open to all candidates.