ASIC SoC Methodology Design Engineer, AI Hardware
TeslaJob Title
ASIC SoC Methodology Design Engineer, AI Hardware
Role Summary
Work on EDA methodology and sign-off flows for large-scale AI inference SoCs and associated systems within Tesla's AI Hardware team. The role focuses on RTL-to-PD integration, lint/CDC/LEC sign-off, and automating flows to improve design quality and sign-off velocity.
This position partners closely with RTL design, DFT, timing, and physical design teams to scale methodology across the full SoC.
Experience Level
Entry-level - requires a minimum of 2+ years of industry experience with digital design EDA tools.
Responsibilities
Typical responsibilities include owning methodology flows and collaborating with design and verification teams:
- Maintain RTL-to-PD integration and Spyglass lint sign-off flows, including filelist methodology and violation triage.
- Set up, run, and maintain Spyglass CDC sign-off flows; configure DFx modes and drive fixes with design teams.
- Define and maintain RTL-to-SDC timing-constraint methodology across diverse IP blocks.
- Manage Spyglass RDC sign-off flows and perform reset-domain analysis with robust reset scenarios.
- Lead Conformal LEC formal equivalence checking and conduct sign-off reviews.
- Automate and improve methodology flows using TCL and Python scripting.
- Collaborate with RTL, DFT, timing, and physical design groups to scale methodologies across the SoC.
Requirements
Must-have technical skills and experience; concise list of expectations.
- 2+ years industry experience with digital-design EDA tools and SoC-scale flows.
- Hands-on experience with Spyglass, Conformal LEC, or equivalent lint/formal tools.
- Practical knowledge of clock-domain crossings, reset synchronization, and synthesis fundamentals.
- Proficiency in TCL and/or Python for flow automation.
- Familiarity with SDC constraint development and RTL filelist management.
- Basic understanding of DFx/DFT scan modes and integration with sign-off flows.
- Experience with Synopsys and Cadence tool ecosystems and physical design flows.
Nice-to-have:
- Experience with large SoC or AI inference chip projects and sign-off at scale.
Education Requirements
Bachelor's degree in Electrical Engineering, Computer Engineering, or equivalent practical experience. The posting explicitly allows "equivalent experience" as an alternative to a degree.
About the Company
Company: Tesla
Headquarters: Palo Alto, California, United States
Tesla is a pioneer in the automotive and energy sectors, known for its innovative electric vehicles and sustainable energy products. The company focuses on integrating cutting-edge technology with artificial intelligence to develop advanced hardware and software solutions, including the Full Self-Driving system and Optimus humanoid robot. Tesla is committed to driving the world's transition to sustainable energy.
