- BS or MS in Computer Science, Storage Systems, or a related technical field, or equivalent experience.
- 12+ years of overall experience in large-scale storage architecture, operations, production engineering, or infrastructure. 6+ years of people management or technical leadership experience with storage, infrastructure, or site reliability teams.
- Deep, protocol-level knowledge of enterprise storage systems spanning block (/NVMe-oF), file (NFS, SMB/CIFS), object (S3-compatible), and high-performance parallel file systems (GPFS/IBM Spectrum Scale, Lustre).
- Hands-on experience deploying and operating solutions from multiple major storage vendors, including NetApp (ONTAP, StorageGRID), Pure Storage (FlashArray, FlashBlade), Cloudian HyperStore, and DDN (EXAScaler, A³I).
- Solid understanding of storage hardware internals — drive types and endurance profiles (NVMe, SAS, SATA, QLC/TLC/MLC NAND), controller architectures, shelf and enclosure design, cabling standards, and failure domain planning.
- Working knowledge of bare-metal server hardware (rack units, HBA/NIC selection, BMC/iDRAC/iLO, firmware management) and data center network hardware relevant to storage connectivity (ToR switches, fiber and copper interconnects, SFP/QSFP optics).
- In-depth expertise in observability tooling: building and maintaining Prometheus exporters, Grafana dashboards, and alerting rulesets for storage fleet health, performance SLOs, and capacity burn-rate tracking.
- Strong configuration management skills using Ansible (playbook authoring, role design, inventory management, Ansible Tower/AAP) for automated provisioning and day-2 operations of storage systems at scale.
- Scripting and automation proficiency in Python and/or Bash; experience integrating with REST APIs to drive CMDB updates, provisioning workflows, and reporting pipelines
- Proven track record of managing large-scale, multi-vendor storage environments (100 PB+) in a fast-paced, high-availability production setting.
Ways to Stand Out from the Crowd
- Deep knowledge of Kubernetes storage integrations — CSI driver operations, persistent volume lifecycle management, StorageClass design for stateful workloads, and experience running storage-intensive workloads on K8s at scale.
- HPC systems expertise: experience deploying and tuning storage for HPC clusters, including parallel file system performance optimization (stripe tuning, client-side caching, OST/MDT balancing) and co-designing storage architectures with HPC compute and fabric teams.
- Familiarity with modern storage technologies (NVMe, RDMA, DPUs) and their impact on system performance, including kernel-level concepts around I/O subsystems and volumes
- Familiarity with HPC job schedulers such as IBM LSF or Slurm — understanding how job scheduling behavior (burst patterns, checkpoint I/O, scratch-space usage) drives storage design decisions, and experience implementing storage-aware scheduling policies.
- Experience building or scaling storage for AI/ML or HPC workloads, including hybrid or multi cloud setups (for example AWS S3, Azure Blob, or Google Cloud Storage), as well as on-prem infrastructure
NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative and autonomous, we want to hear from you!
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary range for this position is 248,000 USD – 396,750 USD.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until November 3, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.