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Physical Design Engineer, Sr Staff

EthosiaHod HaSharon, Center District, IsraelNot specifiedFull-timeSeniority: Not specified

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      Description

      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

      What You'll Need

      • 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)

      Preferred Experience

      • Experience with chiplets, 2.5D/3D IC physical design, or advanced packaging technologies

      • Familiarity with AI/ML-driven EDA tool evaluation and vendor collaboration

      • Prior experience in a tech lead or staff engineering leadership role

      About Ethosia
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