<div>Nava is a neocloud company purpose-built for the AI era. We design, deploy, and operate large-scale GPU infrastructure and deliver inference-as-a-service to teams building the next generation of AI products. Our platform runs on NVIDIA GPU systems, high-performance RDMA fabrics, and a fully automated, software-defined operations model. Every engineer at Nava works close to the metal, on infrastructure built to keep GPUs saturated and models serving.</div><div> </div>

Nava

About Nava&nbsp;&nbsp;
Nava is a neocloud company purpose-built for the AI era. We design, deploy, and operate large-scale GPU infrastructure and deliver inference-as-a-service to teams building the next generation of AI products. Our platform runs on NVIDIA GPU systems, high-performance RDMA fabrics, and a fully automated, software-defined operations model. Every engineer at Nava works close to the metal, on infrastructure built to keep GPUs saturated and models serving.&nbsp;&nbsp;
About the Role&nbsp;
The Compute &amp; Storage Deployment team owns commissioning of NVIDIA GPU servers, supporting CPU infrastructure and high-performance storage, from installed hardware through benchmarked, production-ready capacity.&nbsp;
Lead deployment of NVIDIA GPU servers and high-performance storage systems. Own hardware commissioning, firmware configuration, automated provisioning, GPU diagnostics, storage integration, benchmarking, and production acceptance across distributed clusters.&nbsp;
What You’ll Do&nbsp;
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Lead server installation, BMC/BIOS/firmware baselines, Linux provisioning, NVIDIA drivers and cluster integration.&nbsp;
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Validate GPU topology, PCIe, NVLink/NVSwitch, memory, thermals and hardware health with DCGM and other tools.&nbsp;
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Deploy and tune distributed or parallel storage and validate capacity, throughput, latency and resilience.&nbsp;
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Own burn-in, acceptance benchmarks, NCCL/compute tests, defect closure and documented handover.&nbsp;
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Automate PXE/Redfish-based provisioning, configuration drift controls and repeatable diagnostics.&nbsp;
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Lead engineers and OEM troubleshooting across GPU, server, storage, networking and platform dependencies.&nbsp;
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What You’ll Need&nbsp;
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10–15 years in Linux compute, storage, HPC or large-scale infrastructure deployment.&nbsp;
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Hands-on NVIDIA GPU server and fleet commissioning, BMC/Redfish, firmware and provisioning.&nbsp;
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Practical knowledge of PCIe, NVLink/NVSwitch, GPU drivers and diagnostic workflows.&nbsp;
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Experience with WEKA, Lustre, BeeGFS, IBM Storage Scale, Ceph or similar storage.&nbsp;
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Python/Bash/Ansible automation and strong systems-level debugging.&nbsp;
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Within that, hands-on GPU / AI-datacenter exposure is what we weigh most — we value demonstrated depth over years, as the field is still emerging.&nbsp;
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DGX/HGX or rack-scale NVIDIA systems, Slurm/Kubernetes, cluster-level benchmarking.&nbsp;
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Experience with large-scale hardware infrastructure providers.&nbsp;
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