Seeking a highly talented and experience individual to supervise a core team in the high performance computing environment to manage the server, data storage, software and IT infrastructure in collaboration with our prestigious customers

Key Responsibilities:

Plan and execute HPC and AI application performance tuning and optimization activities in accordance with agreed project schedules and objectives.
Engage with customers and end users to analyse, parallelize and optimize HPC and AI applications, improving performance, scalability and efficient use of computing resources.
Profile and analyse HPC and AI workloads to identify performance bottlenecks across CPU, GPU, memory, I/O and inter-node communication, and recommend appropriate optimization approaches.
Optimize GPU-accelerated and distributed AI workloads, including multi-GPU and multi-node workloads using technologies such as CUDA, PyTorch and distributed training frameworks.
Conduct application benchmarking and scalability analysis to establish performance baselines, evaluate optimization improvements and support efficient utilisation of HPC resources.
Serve as a subject matter expert in HPC and AI application optimization, applying strong technical expertise and industry knowledge to develop optimized solutions that support customer requirements and business objectives.

Job Requirements:

Solid understanding of HPC and AI architectures, including CPU/GPU-based computing, parallel computing and distributed workloads.
Minimum 4 years of relevant experience in application development, performance optimization and numerical model parallelization.
Strong programming and parallel computing skills in Fortran, C, C++, Python, MPI and OpenMP.
Strong working knowledge of UNIX/Linux operating systems and HPC application environments.
Knowledge and experience with numerical simulation applications, such as climate research, weather forecasting, computational science, engineering or aeronautics simulation.
Proficient in using performance analysis, profiling, compiler analysis and debugging tools, such as Linaro Forge, TotalView, NVIDIA Nsight Systems/Nsight Compute, or equivalent tools.
Knowledge of GPU computing and CUDA, including profiling and optimization of GPU-accelerated applications.
Experience with AI/ML frameworks such as PyTorch and/or TensorFlow and running AI workloads in GPU-based computing environments.
Knowledge of distributed AI training and multi-GPU/multi-node workloads, including technologies such as PyTorch Distributed and Distributed Data Parallel (DDP).
Strong communication and presentation skills, with the ability to develop and deliver technical workshops, training and knowledge-sharing sessions to HPC and AI users.

Code Optimizer