Senior CAD Engineer
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Minimum qualifications:
- Bachelor’s degree in Electrical Engineering, Computer Science, Computer Engineering, a related field, or equivalent practical experience.
- 8 years of experience in front-end ASIC design, CAD, or methodology development roles.
- Experience with scripting and automation using languages such as Python or TCL.
Preferred qualifications:
- Experience with scripting for automation (e.g., Shell, Python).
- Familiarity with industry-standard revision control systems (e.g., Git) and CAD data management.
- Strong understanding of RTL design principles, including coding styles, synthesizability, and design-for-verification best practices.
- A strong automation mindset with a demonstrated ability to architect and improve complex engineering workflows.
About the job
The ML, Systems, & Cloud AI (MSCA) organization at Google designs, implements, and manages the hardware, software, machine learning, and systems infrastructure for all Google services (Search, YouTube, etc.) and Google Cloud. Our end users are Googlers, Cloud customers and the billions of people who use Google services around the world.
We prioritize security, efficiency, and reliability across everything we do - from developing our latest TPUs to running a global network, while driving towards shaping the future of hyperscale computing. Our global impact spans software and hardware, including Google Cloud’s Vertex AI, the leading AI platform for bringing Gemini models to enterprise customers.
Responsibilities
- Architect, deploy, and maintain scalable front-end CAD flows and methodologies for SoC designs, including design build, IP delivery, linting, CDC, RDC, and logic synthesis.
- Drive correct-by-construction approaches to detect RTL quality issues early, reducing design iterations and accelerating closure.
- Evaluate and deploy third-party EDA tools, collaborating with vendors to resolve technical issues and enhance feature sets.
- Collaborate cross-functionally with RTL design, verification, and physical design teams to remove bottlenecks and establish best practices.
- Integrate AI/ML-driven flows and agentic workflows to automate verification, speed up coverage closure, and optimize bug detection.
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