AI-Driven ASIC Packaging Automation Engineer

AMD

Austin (TX)

On-site

USD 120,000 - 180,000

Full time

14 days+

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Job summary

AMD is seeking a highly motivated engineer to support the package design organization in developing and deploying AI-driven automation workflows. This role focuses on improving efficiency, quality, and scalability of complex package, bridge, and interposer design, accelerating heterogeneous integration through automation and AI methodologies.

You will collaborate with Design, EDA, and AI/ML teams to translate complex challenges into scalable solutions, work with limited supervision, and

Qualifications

  • Bachelor’s degree in Electrical/Computer Engineering or CS.
  • Experience in semiconductor packaging or SOC physical design automation.
  • Proficiency in scripting: Python (preferred), Tcl, SKILL, or C++.
  • Experience applying AI/ML to engineering workflows.
  • Understanding of DRC/LVS and SI/PI fundamentals.

Responsibilities

  • Develop, deploy, and maintain automation frameworks for package substrates and interposers.
  • Apply AI/ML to auto-routing, placement, signal integrity, and power delivery improvements.
  • Collaborate with Design, EDA, and AI/ML teams.
  • Support design milestones and tape-outs with automation readiness.
  • Document best practices and reusable workflows.

Skills

Analytical skills
Cross-functional collaboration
Communication
Ownership mindset
Autonomy

Education

Bachelor’s or higher in Electrical Engineering / Computer Engineering / Computer Science

Tools

Cadence APD/SiP
Siemens Xpedition
Synopsys 3DIC

Job description

AMD is seeking a highly motivated engineer to support the package design organization in developing and deploying AI-driven automation workflows. This role focuses on improving efficiency, quality, and scalability of complex package, bridge, and interposer design, accelerating heterogeneous integration through automation and AI methodologies.

You will collaborate with Design, EDA, and AI/ML teams to translate complex challenges into scalable solutions, work with limited supervision, and

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