Senior Analog Methodology Lead - AI/Automation

Synopsys, Inc.; Boxborough (MA); On-site; USD 170,000 - 210,000; Full time

Job summary

Synopsys, Inc. is seeking an experienced Analog Methodology Engineer to own and advance mixed-signal methodologies across the analog-to-layout and timing workflows.

You will collaborate with customers, foundries, and product teams to prioritize and deliver robust solutions that accelerate IP deployment for AI workloads. The role emphasizes scripting, automation, and applying AI to optimization flows, with leadership responsibility for method development and cross-domain coordination within the

Qualifications

• Bachelor's degree in Electrical Engineering, Computer Engineering, or similar technical field.
• 15–18 years with an MSEE or PhD; 18–25 years with a BSEE.
• Extensive analog and mixed-signal methodology experience including circuit-to-layout, circuit-to-timing, silicon-to-lab correlation, SI/PI integration.

Responsibilities

• Own analog and adjacent-domain methodology areas and drive adoption.
• Translate customer requirements into integration solutions and actionable plans.
• Lead prioritization, stakeholder engagement, and execution across teams.
• Coordinate with product teams, customers, and foundries to close workflow gaps.
• Collaborate with automation and AI teams to prototype and apply AI to flows.

Skills

Analog/mixed-signal methodology

Scripting & automation

AI/ML in analog design

Transistor-level simulation

Education

Bachelor's degree in Electrical or Computer Engineering

MSEE or PhD preferred

Tools

SPICE

Job description

Synopsys, Inc. is seeking an experienced Analog Methodology Engineer to own and advance mixed-signal methodologies across the analog-to-layout and timing workflows.

You will collaborate with customers, foundries, and product teams to prioritize and deliver robust solutions that accelerate IP deployment for AI workloads. The role emphasizes scripting, automation, and applying AI to optimization flows, with leadership responsibility for method development and cross-domain coordination within the