AI Methodology Design/CAD Engineer
Confirmed on the employer's own hiring board on Nov 1, 2026. First seen by Alion on Oct 29, 2026. Google scores B on the Alion truth index.
About the job
Be part of a team that pushes boundaries, developing custom silicon solutions that power the future of Google's direct-to-consumer products. You'll contribute to the innovation behind products loved by millions worldwide. Your expertise will shape the next generation of hardware experiences, delivering unparalleled performance, efficiency, and integration.
As part of our team you will have opportunities to participate in advancing AI for Chip Design. Automating the chip design workflow is a huge test that can transform the EDA industry. Our opportunity is to build such capabilities, and pioneer next-generation products in collaboration with teams spanning major Product Areas.
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
- Manage fine-tune AI agents by refining prompt strategies, toolsets, core skills, and model selection.
- Design and build CAD tools and integrated workflows that allow AI agents to seamlessly interact with EDA software and design data.
- Partner with design teams and CAD flow leads across different projects to pinpoint complex design hurdles and evaluate AI integration opportunities.
- Develop evaluation datasets and quality metrics, executing model evaluations to uncover agent failure modes.
- Assess vendor EDA AI tools and benchmark them against internal proprietary solutions.
Qualifications
Minimum qualifications:
- Bachelor’s degree in Electrical Engineering, Computer Science, a related technical field, or equivalent practical experience.
- 8 years of experience with standard ASIC flows and Chip EDA tools.
- Experience using scripting languages such as Python, Tcl, and Unix Shell to architect automation tools and infrastructure.
Preferred qualifications:
- Proficiency with modern revision control systems, such as Git.