Senior Staff Infrastructure Engineer - Virtualization
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 compute, memory, storage, and network layers
Contribute to long-term platform architecture and infrastructure strategy
Who You Are
Required Qualifications
7+ years of experience in infrastructure, systems engineering, or platform engineering
Deep experience with Linux-based virtualization, including:
KVM/QEMU
libvirt or similar tooling
Strong understanding of:
CPU scheduling and NUMA architectures
Memory management and performance tuning
Storage I/O paths and performance characteristics
Experience designing and operating virtualization platforms at scale (hundreds+ hosts)
Solid networking fundamentals, including:
Linux networking (bridges, bonding, VLANs)
High-performance networking concepts
Experience with infrastructure automation (e.g., Ansible, Terraform, or similar)
Strong troubleshooting skills across distributed systems
Preferred Qualifications
Experience in cloud or CSP environments (public or private)
Familiarity with:
GPU workloads and passthrough (VFIO)
SR-IOV and advanced NIC features
Experience integrating virtualization with:
Kubernetes platforms
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 We Offer
Stock Options
100% paid Medical, Dental, and Vision insurance for Employees
Company Health Savings Account Contributions
100% paid Short Term and Long Term Disability Insurance for Employees
Life and Voluntary Supplemental Insurance Options
Other Insurance Options, such as Pet & Legal Insurance
Various Supplementary Health Benefits, such as discounted Virtual Healthcare Appointments and Serious Illness Support
Flexible Spending Account
401(k)
Employee Assistance Program
Flexible PTO
Paid Holidays
Parental Leave
Other In-Office Perks
Equal Employment Opportunity
TensorWave is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate on the basis of any protected status under applicable law.