Etched.ai
Physical Design Engineer
San Jose$150k–$275kfulltimemidAdded today
About this role
Etched seeks an experienced Physical Design Engineer to lead block-level chip implementation and optimization for AI inference hardware. You'll drive timing closure, manage design flows, collaborate with RTL teams, and oversee third-party design work while helping scale the company's design infrastructure.
What you'll do
- Own block-level physical design implementation and verification through GDSII sign-off
- Drive timing closure and PPA optimization while maintaining convergence dashboards
- Collaborate with RTL designers to provide feedback and improve design performance
- Supervise and manage third-party physical design outsourcing
- Automate and optimize EDA tool flows in coordination with vendors
- Support ASIC infrastructure and improve CAD tools and design methodology
What they're looking for
- 5-10+ years physical design experience on advanced nodes (5nm and below)
- Cadence (Innovus, Genus) or Synopsys (ICC2, Fusion Compiler) tools
- Sign-off tools (PrimeTime, Tempus, Voltus)
- UPF-based low power design, timing closure, and floorplanning/placement/routing
- Python or scripting language for flow automation
- Formal verification, scan insertion, IR drop, and EM/antenna analysis
- Knowledge of ML/LLM architectures (preferred)
- First-principles problem-solving and creative thinking
Benefits
- Medical, dental, and vision insurance with generous coverage ($500/month credit option)
- $2,000/month housing subsidy for office-proximate employees
- Relocation support to San Jose (Santana Row)
- Daily lunch and dinner provided in office
- Wellness benefits covering fitness and mental health
- Unlimited compute budget subject to ROI justification
Likely interview questions
- Walk us through your experience with RTL-to-GDSII flows using Cadence or Synopsys tools. What were the biggest challenges you faced in timing closure on advanced nodes?
- Describe a time when you optimized PPA for a block. What tradeoffs did you make and how did you validate your decisions?
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