AI-Driven Physical Design Lead for Advanced SoCs

Alexander Chapman Ltd; San Francisco (CA); Sur place; USD 180 000 - 260 000; Plein temps

Résumé du poste

Alexander Chapman Ltd in San Francisco seeks an experienced Implementation Lead to drive end-to-end physical design of advanced silicon and AI hardware. You will own floorplanning, CTS, routing, and timing closure for high-performance ASICs/SoCs, while pushing AI-driven optimization and automation across large-scale designs.

The role requires 5+ years in physical design, strong scripting (Python/Tcl), and expertise with EDA toolchains.

Qualifications

• 5+ years hands-on physical design experience.
• Ownership of complex ASIC or SoC implementations through tape-out.
• Strong Python and Tcl scripting for PD automation.
• Experience with advanced-node implementation and EDA tools.
• Exposure to AI/ML-driven EDA optimization and design-space exploration.

Responsabilités

• Lead physical implementation of complex ASICs and compute subsystems from floorplanning through tape-out.
• Own placement, clock tree synthesis, routing, timing closure, and physical sign-off.
• Develop and optimize implementation methodologies for large-scale designs.
• Integrate machine learning and automated optimization into PD workflows to improve PPA and efficiency.
• Address power integrity, thermal, congestion, and timing challenges.
• Develop scalable automation frameworks to accelerate design iterations and improve quality.

Connaissances

Physical design

ASIC / SoC

Floorplanning

Placement

Clock tree synthesis

Routing

Timing closure

EDA tools

Python scripting

Tcl scripting

AI/ML optimization

Automation frameworks

Power integrity

Thermal analysis

Outils

Synopsys Suite

Cadence Innovus

Mentor Graphics

Description du poste

Alexander Chapman Ltd in San Francisco seeks an experienced Implementation Lead to drive end-to-end physical design of advanced silicon and AI hardware. You will own floorplanning, CTS, routing, and timing closure for high-performance ASICs/SoCs, while pushing AI-driven optimization and automation across large-scale designs.

The role requires 5+ years in physical design, strong scripting (Python/Tcl), and expertise with EDA toolchains.