TensorWave
AI compute
StaffInfrastructureEngineer-Virtualization
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“Staff Infrastructure Engineer - Virtualization at TensorWave. Skills: Virtualization platform design and evolution, KVM/QEMU-based architectures, Hypervisor infrastructure at scale, High-performance and GPU workloads, Infrastructure automation. Design and implement a scalable virtualization platform capable of supporting high-density compute and GPU workloads. Lead the evolution from existing platforms (e.g., Proxmox) toward KVM/QEMU-based architectures”
Industry & Context.
solving complex systems problems at scale; Troubleshoot deep system-level performance issues
Employment eligibility verification, Background checks, Data privacy acknowledgement
What They're Looking For.
Must Have
7+ years of experience in infrastructure, systems engineering, or platform engineering, Deep experience with Linux-based virtualization, KVM/QEMU, libvirt or similar tooling, understanding of CPU scheduling and NUMA architectures, understanding of Memory management and performance tuning, understanding of Storage I/O paths and performance characteristics, Experience designing and operating virtualization platforms at scale (hundreds+ hosts), Solid networking fundamentals, Linux networking (bridges, bonding, VLANs), High-performance networking concepts, Experience with infrastructure automation (e.g., Ansible, Terraform, or similar), troubleshooting skills across distributed systems
Nice to Have
Experience in cloud or CSP environments (public or private), GPU workloads and passthrough (VFIO), SR-IOV and advanced NIC features, Experience integrating virtualization with Kubernetes platforms, Experience integrating virtualization with Bare metal provisioning systems (e.g., MAAS), Exposure to distributed storage systems (e.g., Ceph, Weka, or similar), Experience working in high-performance or low-latency environments
What You'll Do.
Design and implement a scalable virtualization platform capable of supporting high-density compute and GPU workloads
Lead the evolution from existing platforms (e.g.
Proxmox) toward KVM/QEMU-based architectures
Define standards for VM lifecycle management (provisioning
performance isolation and resource allocation
failure domains and resilience strategies
Optimize virtualization for high-performance workloads
including NUMA alignment
CPU pinning and scheduling
PCIe topology awareness
GPU passthrough and device assignment
Partner closely with networking and storage teams to integrate high-throughput
distributed and local storage systems
Build and improve automation for hypervisor deployment and configuration
cluster scaling and lifecycle management
Troubleshoot deep system-level performance issues across compute
Contribute to long-term platform architecture and infrastructure strategy
How You'll Work.
Team & Collaboration
Partner closely with networking and storage teams
Full Job Description
About TensorWave Our mission is simple: deliver seamless, secure, reliable, and resilient AI compute at scale. We've built a versatile cloud platform that eliminates infrastructure barriers, empowering builders to focus on innovation instead of fighting their stack. Because breakthrough AI should move at the speed of ideas, not infrastructure. About the Role We are building large-scale, high-performance infrastructure to power next-generation AI workloads. Our platform operates across multiple data centers and supports GPU-intensive environments with demanding requirements around performance, isolation, and scalability. We are looking for a Staff Infrastructure Engineer to lead the design and evolution of our virtualization platform. This role will own how we build, scale, and operate hypervisor infrastructure as we transition from traditional virtualization platforms toward a more flexible, CSP-aligned architecture based on KVM/QEMU and modern Linux primitives. This is a highly technical, hands-on role focused on solving complex systems problems at scale. What You’ll Do - Design and implement a scalable virtualization platform capable of supporting high-density compute and GPU workloads - Lead the evolution from existing platforms (e.g., Proxmox) toward KVM/QEMU-based architectures - Define standards for VM lifecycle management (provisioning, scheduling, migration), performance isolation and resource allocation, failure domains and resilience strategies - Optimize virtualization for high-performance workloads, including NUMA alignment, CPU pinning and scheduling, PCIe topology awareness, GPU passthrough and device assignment - Partner closely with networking and storage teams to integrate high-throughput, networking (e.g., SR-IOV, RDMA), distributed and local storage systems - Build and improve automation for hypervisor deployment and configuration, image pipelines, cluster scaling and lifecycle management - Troubleshoot deep system-level performance issues across
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