<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;
This is a principal individual-contributor role on the Architecture function. You architect coherent, deployable GPU cluster designs across compute, network, storage, management and physical infrastructure constraints.&nbsp;
Architect end-to-end AI infrastructure integrating GPU compute, high-performance networking, distributed storage, and management systems. Define reference architectures, capacity models, performance standards, resiliency requirements, and scalable cluster deployment designs.&nbsp;
What You’ll Do&nbsp;
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Create reference architectures and low-level designs spanning NVIDIA GPU topologies, high-speed fabrics, storage and management planes.&nbsp;
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Translate AI workload and scale targets into network bandwidth, storage IOPS/throughput, rack count, power and cooling envelopes.&nbsp;
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Define topology, failure domains, resilience patterns, lifecycle constraints and expansion strategy.&nbsp;
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Evaluate trade-offs among NVLink/NVSwitch, InfiniBand/RoCE, parallel storage and orchestration options.&nbsp;
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Define engineering standards, bill-of-material inputs, performance acceptance tests and design review gates.&nbsp;
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Validate designs with deployment and SRE teams; drive improvements from field results and benchmark evidence.&nbsp;
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What You’ll Need&nbsp;
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7–12 years designing production cloud, HPC or high-performance distributed infrastructure.&nbsp;
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Deep technical strength in at least two of GPU systems, data center networking, distributed storage or Linux infrastructure.&nbsp;
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Working grasp of NVIDIA architectures, GPU communication, network fabrics and data center constraints.&nbsp;
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Ability to produce implementable low-level designs and capacity/performance models.&nbsp;
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Evidence of architecture decisions deployed and operated 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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