<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;
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;
About the Role&nbsp;
We are building a next-generation Infrastructure Observability Platform that provides a unified view of modern datacenter and cloud infrastructure across physical, network, compute, GPU, storage, and software layers. The platform will build a rich infrastructure graph linking assets, dependencies, workloads, tenants, and services, enabling real-time visibility into health, capacity, performance, topology, and utilization. The role involves designing and building scalable data pipelines, asset and topology models, telemetry ingestion, correlation, and intelligent analytics for large-scale infrastructure environments. You will work across distributed systems, cloud infrastructure, Kubernetes, GPUs, networking, and data platforms to create a platform that turns infrastructure data into actionable operational intelligence.&nbsp;
What You’ll Do &nbsp;
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Design and build fleet automation systems that provision, validate, deploy, upgrade, repair, and retire GPU clusters with minimal human intervention.&nbsp;
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Build AI Infrastructure Agents that automate deployment, root-cause failures, incident triage, and autonomous remediation.&nbsp;
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Develop Fleet Intelligence platforms that continuously monitor hardware health, firmware, networking, storage, thermals, and workload performance to predict failures before they impact customers.&nbsp;
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Build software that maximizes GPU availability, utilization, performance, and reliability across thousands of accelerators.&nbsp;
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Create automated validation systems for GPUs, InfiniBand/RoCE fabrics, NVLink/NVSwitch, storage, and distributed AI workloads.&nbsp;
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Build internal platforms and developer tools that allow infrastructure to be managed through software—not manual operations.&nbsp;
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Continuously improve deployment velocity, reliability, and operational efficiency through automation.&nbsp;
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Partner closely with hardware, networking, platform, and AI teams to push the limits of AI infrastructure.&nbsp;
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What You’ll Need &nbsp;
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4-7 years building distributed systems, infrastructure platforms, or large-scale backend software.&nbsp;
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Strong software engineering skills in Go, Python.&nbsp;
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Experience building platforms, automation systems, or developer infrastructure.&nbsp;
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Experience with Linux, Kubernetes, Terraform, Ansible, or similar infrastructure technologies.&nbsp;
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Strong systems thinking with the ability to understand problems across hardware and software.&nbsp;
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A passion for solving complex infrastructure challenges through software.&nbsp;
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An automation-first mindset—if a task is repeated, your instinct is to build a system to eliminate it.&nbsp;
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