We are seeking an experienced HPC Engineer to design, deploy, and maintain a high-performance computing (HPC) cluster for our AI training workloads. The successful candidate will be responsible for setting up a GPU-based training cluster together with our Research team, and ensuring that works well with our Model Training Algorithms.
Key Responsibilities:
- Design and deploy a GPU-based HPC cluster using industry-standard components (e.g., NVIDIA DGX/HGX, or similar), including the design of nodes (e.g. NVLink, SXM)
- Configure and optimise the cluster for high-performance computing, focusing on AI workloads (e.g., PyTorch, Torch or similar).
- Implement and manage cluster management software (e.g., Kubeflow, Slurm or similar).
- Design cluster for high-bandwidth, low-latency network performance in GPU clusters (InfiniBand, Ethernet RDMA, and/or RoCE), using scalable and efficient network topologies (Fat Tree, Dragonfly, and/or Torus)
- Troubleshoot and resolve issues related to cluster performance, hardware failures, and software glitches.
Requirements
- Bachelor's or Master's degree in Computer Science, Computer Engineering, or a related field
- Extensive experience in designing, assembling, and configuring high-performance computing systems
- Proficient in selecting and integrating HPC hardware components, including CPUs, GPUs, memory, storage, and interconnects
- Strong knowledge of HPC software stacks, including operating systems, drivers, and specialised applications
- Experience in designing and operating AI training clusters, including the selection and integration of the necessary hardware and software components
- Expertise in conducting comprehensive benchmarking tests and analyzing performance data
- [Plus] Strong networking knowledge, including experience with high-speed interconnects such as Infiniband, RoCE Ethernet, and RDMA
- [Plus] Experience with setting up and managing Nvidia multi-node training clusters for machine learning applications
