About the company
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.
Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D.
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).
- 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.
Conditions
- 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