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We’re looking for a Senior Software Systems Engineer to join our team and play a key role in the development of our cutting-edge hyperscaler platform. The GPU & InfiniBand team is responsible for enhancing and optimizing the core components of our Cloud platform, with a specific focus on GPU computing, InfiniBand networks, and the KVM/QEMU stack. You’ll work closely with hardware virtualization and device emulation technologies, ensuring high performance and security in multi-GPU, HPC environments. The role involves analyzing, troubleshooting, and improving infrastructure to support new hardware, fine-tuning system performance, and automating fault detection and resolution in a complex system. In this position, you will be responsible for: Tuning the performance of GPU clusters and InfiniBand networks to ensure optimal operation in HPC and GPU-based environments. Analyzing and troubleshooting the root cause of issues related to GPUs and InfiniBand networks, and proposing corrective actions. Integrating new hardware into the existing infrastructure, including support for new GPU hardware through software stacks like Kubernetes, QEMU, and KVM. Enhancing automation systems for proactive monitoring, detecting, and resolving issues in GPU and InfiniBand environments. Configuring and managing GPU devices and InfiniBand fabrics, ensuring efficient and reliable operation. We expect you to have: 5+ years of professional experience in system-level software development (focused on performance optimization, low-level programming). 3+ years of hands-on experience with Linux systems (administration, troubleshooting, and performance tuning). In-depth understanding of server architecture, including PCIe devices, NICs, Linux OS/Kernel, and high-performance computing (HPC) systems. Strong proficiency in one or more performance-oriented programming languages (C/C++, Go, Python). It would be a plus if you have: Experience with GPU end-to-end testing in a cluster environment using InfiniBand networking. Proven track record of analyzing and optimizing the performance of HPC workloads (e.g., simulations, data analysis, AI/ML workloads). Familiarity with RDMA, RoCE, and InfiniBand protocols for high-performance communication. Background in Software-Defined Networking (SDN) and experience with HPC cluster networking. Understanding of QEMU/KVM virtualization and managing virtualized environments. Experience with deep learning frameworks such as PyTorch and TensorFlow, and their integration with HPC systems. Familiarity with collective communication libraries like MPI and NCCL for distributed computing. We offer competitive salaries ranging from $170k-$300k + equity based on your experience. We conduct coding interviews as part of the process. #LI-LH2