<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 SRE &amp; Operations team keeps NVIDIA GPU fleets and storage available for production AI workloads, combining Linux expertise, hardware diagnosis, distributed systems reliability and recovery automation.&nbsp;
Operate production GPU compute and storage systems. Diagnose hardware failures, GPU errors, and storage bottlenecks. Automate recovery, improve observability, manage incidents, and optimize infrastructure reliability and performance.&nbsp;
What You’ll Do&nbsp;
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Monitor GPU errors, node health, driver and OS stability, storage latency and capacity.&nbsp;
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Investigate Xid/ECC, PCIe, NVLink, hardware, firmware and Linux failures.&nbsp;
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Troubleshoot storage outages and performance degradation in parallel/distributed systems.&nbsp;
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Create automated node isolation, remediation, repair and validation workflows.&nbsp;
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Improve DCGM/system telemetry, incident response, runbooks and root-cause analysis.&nbsp;
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Partner with platform teams, network SRE and OEMs to restore customer workloads.&nbsp;
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What You’ll Need&nbsp;
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5–8 years in Linux systems, distributed infrastructure or SRE.&nbsp;
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Experience debugging bare-metal servers, storage, firmware and Linux incidents.&nbsp;
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Python/Go/Bash automation, monitoring and operational discipline.&nbsp;
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Understanding of Kubernetes or Slurm and server fleet management.&nbsp;
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Hands-on experience operating NVLink / NVSwitch domains and GB-class nodes (NVIDIA GB200 / GB300 NVL72 rack-scale systems) in production.&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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