The Ecosystems Engineering group is seeking a Senior Integration Engineer to bridge the gap between cutting-edge Nvidia hardware and open-source software stack. This is a highly technical, "hands-on" role for an engineer who thrives at the intersection of the Linux kernel, virtualization, and high-performance networking.
Rather than focusing purely on application logic, you will be responsible for hardware enablement and system-level integration. You will ensure that GPUs, DPUs, and other accelerators are seamlessly orchestrated within OpenShift and Virtualization environments. If you are a "problem solver" who enjoys debugging the complex layers between a physical NIC and a virtualized AI workload, this role is for you.
What You Will Do
Lead the integration of Nvidia hardware (GPUs, DPUs) into the ecosystem, ensuring drivers, firmware, and orchestration layers work in harmony.
Build and optimize solutions using KVM, QEMU, and libvirt to ensure high-performance hardware pass-through and abstraction.
Design and implement robust networking paths using Advanced Software Defined Network (SDN) and Virtual Networking
Act as the technical bridge between hardware-level drivers and cloud-native platforms like Kubernetes and OpenShift.
Develop integration patterns and "golden paths" for AI workloads, ensuring they meet strict performance and resiliency requirements.
Work closely with Product Engineering, Partners (Nvidia), and Customers to root-cause complex issues across the entire stack (Hardware → Kernel → Hypervisor → Container).
Create architectural blueprints and implementation guides for field engineers and lighthouse customers.
Explore and experiment with emerging AI technologies relevant to software development, proactively identifying opportunities to incorporate new AI capabilities into existing workflows and tooling.
Rather than focusing purely on application logic, you will be responsible for hardware enablement and system-level integration. You will ensure that GPUs, DPUs, and other accelerators are seamlessly orchestrated within OpenShift and Virtualization environments. If you are a "problem solver" who enjoys debugging the complex layers between a physical NIC and a virtualized AI workload, this role is for you.
What You Will Do
Lead the integration of Nvidia hardware (GPUs, DPUs) into the ecosystem, ensuring drivers, firmware, and orchestration layers work in harmony.
Build and optimize solutions using KVM, QEMU, and libvirt to ensure high-performance hardware pass-through and abstraction.
Design and implement robust networking paths using Advanced Software Defined Network (SDN) and Virtual Networking
Act as the technical bridge between hardware-level drivers and cloud-native platforms like Kubernetes and OpenShift.
Develop integration patterns and "golden paths" for AI workloads, ensuring they meet strict performance and resiliency requirements.
Work closely with Product Engineering, Partners (Nvidia), and Customers to root-cause complex issues across the entire stack (Hardware → Kernel → Hypervisor → Container).
Create architectural blueprints and implementation guides for field engineers and lighthouse customers.
Explore and experiment with emerging AI technologies relevant to software development, proactively identifying opportunities to incorporate new AI capabilities into existing workflows and tooling.
Requirements:
+4 years of experience in system integration, infrastructure engineering, or specialized DevOps.
Deep practical knowledge of server virtualization technology (ESX, Hyper-V, KVM)
Strong understanding of Software Defined Networking (SDN) concepts.
Hands-on experience with Kubernetes, Podman, or Docker, specifically regarding how containers consume host resources and hardware.
Advanced proficiency in Linux system administration, kernel modules, and hardware-software interfaces.
Practical experience implementing secured infrastructure principles (Zero Trust, Micro-segmentation, eVPN)
A passion for "how things work together" and the ability to troubleshoot across multiple engineering domains (Network, Storage, Compute).
Ability to work closely with diverse teams and translate complex hardware requirements into software-defined solutions.
Excellent system understanding and troubleshooting capabilities.
Autonomous work ethic, thriving in a dynamic, fast-paced environment.
Proficient written and verbal communication skills in English.
The Following is Considered a Plus
Experience with cloud administration for public cloud services (AWS, GCE, Azure).
Deep practical knowledge of KVM, QEMU, and libvirt. Experience with ESXi is a plus.
Hands-on experience with Nvidia GPU or Networking
Background in DevOps or site reliability engineering (SRE).
Experience with Operators and AI workload deployments ( LLM , vLLM , inference, agents )
Recent hands-on experience with distributed computation, either at the end-user or infrastructure provider level.
Experience with performance analysis tools.
+4 years of experience in system integration, infrastructure engineering, or specialized DevOps.
Deep practical knowledge of server virtualization technology (ESX, Hyper-V, KVM)
Strong understanding of Software Defined Networking (SDN) concepts.
Hands-on experience with Kubernetes, Podman, or Docker, specifically regarding how containers consume host resources and hardware.
Advanced proficiency in Linux system administration, kernel modules, and hardware-software interfaces.
Practical experience implementing secured infrastructure principles (Zero Trust, Micro-segmentation, eVPN)
A passion for "how things work together" and the ability to troubleshoot across multiple engineering domains (Network, Storage, Compute).
Ability to work closely with diverse teams and translate complex hardware requirements into software-defined solutions.
Excellent system understanding and troubleshooting capabilities.
Autonomous work ethic, thriving in a dynamic, fast-paced environment.
Proficient written and verbal communication skills in English.
The Following is Considered a Plus
Experience with cloud administration for public cloud services (AWS, GCE, Azure).
Deep practical knowledge of KVM, QEMU, and libvirt. Experience with ESXi is a plus.
Hands-on experience with Nvidia GPU or Networking
Background in DevOps or site reliability engineering (SRE).
Experience with Operators and AI workload deployments ( LLM , vLLM , inference, agents )
Recent hands-on experience with distributed computation, either at the end-user or infrastructure provider level.
Experience with performance analysis tools.
This position is open to all candidates.
