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# Emulation Front-End Infrastructure Engineer

- **Company:** NVIDIA Corporation
- **Location:** Santa Clara, CA, United States
- **Experience:** Experienced
- **Salary:** $136,000.0 - $218,500.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Computer Engineering, Software Debugging, Electronic Design Automation, Perl (Programming Language), Emulators, Make (Software), Hardware Description Language, Python (Programming Language), Perforce, Shell Script, SystemVerilog, Tcl (Programming Language), Verilog, Large Language Models, Git, Slurm, Front End Software Development
- **Published:** October 12, 2026
- **Apply:** https://www.juju.com/job/1_ScrapingHub33470-JR2023066

## About the Role

What we need to see: + A BS degree in Electrical Engineering, Computer Engineering, or a related field (or equivalent experience) with 5+ years of relevant experience; or an MS degree in a related field with 3+ years of relevant experience. + Experience with RTL compilation, synthesis, front-end design implementation, and flow automation. + Working proficiency in Verilog and SystemVerilog, including the ability to read, debug, and modify RTL for emulation needs. + Strong understanding of RTL elaboration, synthesis, logic optimization, and front-end design methodologies. + Hands-on experience with front-end synthesis and compilation EDA tools, including Synopsys Design Compiler, ZeBu Compiler, and Cadence HDL-ICE. + Strong automation and scripting skills in Python, Perl, Tcl, shell scripting, and Makefile-based flows. + Experience running high volumes of jobs on distributed compute farms using workload schedulers such as LSF or Slurm, and using revision-control systems such as
Perforce or Git. + Strong debugging, problem-solving, communication, and cross-functional collaboration skills.
Ways to stand out from the crowd: + Experience with hardware-emulation compilation flows and runtime bring-up or debugging is a plus! + Deep expertise with Synopsys Design Compiler, ZeBu Compiler, and Cadence HDL-ICE. + Experience building scalable regression, reporting, monitoring, and flow-debug infrastructure. + Experience building AI/LLM agents or automation that can classify tool logs, diagnose failures, and drive automated recovery. + Experience partnering directly with EDA vendors to resolve complex tool, methodology, or performance issues.