VLSI CAD Engineer

Overview

• NVIDIA is looking for an exceptional engineer to grow and thrive alongside our CAD/EDA/HPC team.
• You will build and scale the compute infrastructure that powers NVIDIA's next-generation silicon — owning job scheduler environments, cloud compute integration, CAD toolchains, and automation frameworks that keep our design teams moving at full speed toward tapeout.
• What you'll be doing: Be part of the CAD/EDA/HPC team building and scaling the compute infrastructure that powers NVIDIA's next-generation silicon design.
• Own job scheduler environments, CAD toolchains, automation frameworks, and operational workflows that keep design teams moving efficiently toward tapeout.
• Integrate and operate hybrid cloud environments across AWS, Azure, GCP, or OCI to elastically extend on-premises CAD capacity.
• Troubleshoot CAD/EDA software and infrastructure performance issues, benchmark workloads, and improve tool and compute efficiency.
• Build automation in Python, Perl, Bash, or Tcl for job scheduling, monitoring, capacity reporting, and recurring operational workflows.
• Operate large-scale Linux compute farms using LSF and/or Slurm while partnering with design teams on throughput, utilization, and tapeout capacity planning.
• What we need to see: B.E./B.Tech or M.Tech/M.S.
• in Computer Science, Electronics Engineering, or a related field, or equivalent experience.
• 3+ years of hands-on experience in VLSI CAD infrastructure, EDA compute environments, HPC system administration, or SRE roles supporting engineering infrastructure.
• Strong Linux/Unix administration skills, large-scale compute farm experience with LSF and/or Slurm, and proficiency in at least one scripting language; Python is preferred.
• Hands-on knowledge of cloud platforms such as GCP, OCI, AWS, or Azure, including compute, storage, networking, and cost fundamentals.
• Good understanding of CAD/EDA flows such as synthesis, P&R, simulation, DRC/LVS, or equivalent implementation and verification flows.
• Preferred exposure to Linux performance engineering, Docker/Kubernetes, infrastructure as code such as Ansible or Terraform, distributed file systems, and observability stacks.