NVIDIA is looking for an exceptional engineer to grow and thrive alongside our Infrastructure team that designs, evaluates, and optimizes the compute inrastructure powering NVIDIA's next-generation silicon design and AI
workloads.You will own the operational health, efficiency and observability of a large Linux compute farm serving NVIDIA's VLSI design teams
What you'll be doing:
- In this position, you will own the operational health, efficiency and observability of a large Linux compute farm serving NVIDIA's VLSI design teams, and be the person who can say where the time and the capacity are actually going.
- Profile and debug at the system level - kernel, filesystem, memory and I/O behaviour - when workloads are slow, and turn findings into fixes rather than tickets.
- Drive compute efficiency: memory request-versus-usage accuracy, CPU waste from idle and over-provisioned jobs, and the prediction models behind resource requests.
- Build the observability layer for the farm - telemetry pipelines, metrics and dashboards that make capacity, utilisation and job behaviour visible to engineers and to leadership - and own licence monitoring and expiry alerting across the EDA tool estate.
- This is a hands-on technical role. Day-to-day farm operations and live triage are a core part of it, and you will work directly with the heaviest users on their workload behaviour, since most efficiency gains come from a small number of workflows.
What we need to see:
- B.E./B.Tech/M.Tech in Computer Science, Electrical Engineering, or equivalent experience.
- 8+ years in Linux systems administration or infrastructure operations at scale.
- Deep Linux systems and kernel-level expertise - comfortable with perf, eBPF/bpftrace, strace, cgroups, NUMA, memory management, scheduler behaviour and I/O subsystems, and able to use them to find root cause rather than guess at it.
- Demonstrated experience diagnosing and fixing performance problems on large multi-user compute systems.
- Strong NFS and distributed filesystem troubleshooting: metadata versus data path, client behaviour, mount and latency issues.
- Hands-on experience building observability - time-series metrics, log and telemetry pipelines, and dashboards (Prometheus/Grafana, Elastic, or equivalent) - plus strong Python for analysis and automation.
- Familiarity with batch schedulers (LSF, Slurm, or equivalent) and job resource accounting, and a genuine appetite for operational work. Semiconductor/EDA or HPC background is a plus.