NVIDIA
Emulation Front-End Infrastructure Engineer
$136K–$264K
US, CA, Santa Clara · Full-time · Posted Aug 10, 2026
- Experience
- BS degree or equivalent experience: 5+ years of relevant experience; or MS degree in a related field: 3+ years of relevant experience.
- Education
- BS in Electrical Engineering, Computer Engineering, or a related field (or equivalent experience); or MS in a related field.
About the job
Build and maintain front-end compilation, synthesis, and automation infrastructure for NVIDIA hardware emulation and pre-silicon validation.
What you'll do
- Develop, automate, debug, and improve compilation and synthesis flows, including regressions and EDA tool support.
- Diagnose compilation, elaboration, synthesis, and implementation issues with design teams and EDA vendors.
- Apply AI techniques to automate flow tasks, triage failures, identify patterns, and accelerate root-cause analysis.
What they ask for
- Experience with RTL compilation, synthesis, front-end design implementation, and flow automation.
- Working proficiency in Verilog and SystemVerilog, including reading, debugging, and modifying RTL.
- Hands-on experience with Synopsys Design Compiler, ZeBu Compiler, and Cadence HDL-ICE.
- Automation and scripting skills in Python, Perl, Tcl, shell scripting, and Makefile-based flows; experience with distributed compute schedulers and revision-control systems.
Nice to have
- Experience with hardware-emulation compilation flows and runtime bring-up or debugging.
- Experience building scalable regression/reporting infrastructure or AI/LLM agents for log classification, failure diagnosis, and automated recovery.
Pay
136,000 USD - 218,500 USD base salary; 168,000 USD - 264,500 USD base salary
Tools
Verilog, SystemVerilog, Synopsys Design Compiler, ZeBu Compiler, Cadence HDL-ICE, Python, Perl, Tcl
AI focus
Apply AI techniques to automate flow tasks, triage compilation failures, identify recurring patterns, and accelerate root-cause analysis; experience building AI/LLM agents for log classification, failure diagnosis, and automated recovery is preferred.
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