
Physical Design Engineer, Sr Staff – 3092360
עלתה ל-JOBTIME לפני 12 שעות· בתוקף עד 24 בנובמבר 2026
- מיקום
- כל הארץ
- היקף משרה
- משרה מלאה
- תפקיד
- מהנדס/ת חומרה ואלקטרוניקה
תיאור המשרה
What You%27ll Do
- Architect and own the full-chip physical design strategy: floorplanning, power delivery network %28PDN%29, clock architecture, PnR, and timing closure
- Define and drive backend methodology standards across the team for advanced node tapeouts %28N5/N4P/N3%29
- 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 %28Synopsys, Cadence, Siemens%29, 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%27ll Need
- 10+ years of experience in digital Physical Design with a proven track record of tapeouts on FinFET nodes %28N7 and below%29
- 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 %28DVFS, multi-Vt, power domains, UPF/CPF%29
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