- Location
- India, Bengaluru · India, Hyderabad
- Workplace
- Remote, Hybrid
- Type
- Full-time
- Department
- Engineering
- Education
- Master
- Source
- Workday
Description
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.
