<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 commissions NVIDIA GPU servers and high-performance storage systems, transforming bare-metal hardware into configured, diagnosed, benchmarked and accepted compute capacity.&nbsp;
Deploy NVIDIA GPU servers and distributed storage infrastructure. Perform hardware commissioning, firmware configuration, Linux provisioning, GPU diagnostics, storage integration, performance benchmarking, automation, troubleshooting, and production readiness validation.&nbsp;
What You’ll Do&nbsp;
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Bring up GPU servers, firmware, BMC, Linux images, CUDA drivers and operating system services.&nbsp;
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Validate GPU PCIe/NVLink connectivity, DCGM health and hardware burn-in results.&nbsp;
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Deploy parallel/distributed storage and test capacity, bandwidth, latency and recovery.&nbsp;
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Automate PXE/Redfish provisioning, baselines, diagnostics and configuration checks.&nbsp;
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Run GPU/cluster and storage acceptance benchmarks and close faults with OEMs.&nbsp;
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Document test results and hand over production-ready infrastructure.&nbsp;
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What You’ll Need&nbsp;
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5–8 years in Linux, server infrastructure, HPC or storage engineering.&nbsp;
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Hands-on bare-metal provisioning, firmware management and hardware troubleshooting.&nbsp;
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Linux/Python/Bash/Ansible capabilities and distributed storage fundamentals.&nbsp;
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Experience with server OEM tools, BMCs and hardware acceptance tests.&nbsp;
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Hands-on NVLink / NVSwitch bring-up and validation, and experience commissioning GB-class nodes (NVIDIA GB200 / GB300 NVL72 rack-scale systems).&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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<li>NVIDIA GPU/DCGM/NCCL, WEKA, Lustre, BeeGFS, IBM Storage Scale or Ceph and Kubernetes/Slurm.&nbsp;</li>
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