HPC engineer in GPU computing

Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI.

• 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.

• 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).
• Будет плюсом: 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

• Competitive compensation
• Career growth and learning opportunities
• Flexibility and ownership
• Collaborative and innovative culture
• Opportunity to work on impactful AI projects
• International environment and talented teams
• Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI