Senior Systems Software Engineer, GPU Compute

Nebius is leading a new era in cloud infrastructure for the global AI economy, and they are seeking a Senior Systems Software Engineer to join their GPU & InfiniBand team. The role focuses on enhancing and optimizing core components of their cloud platform, particularly in GPU computing and InfiniBand networks, while ensuring high performance and security in multi-GPU environments.

Responsibilities:

• 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

Requirements:

• 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)
• 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