Requirements:

B.Sc in Computer Science or Software Engineering or equivalent experience
5+ years of experience with high-performance Networking (RDMA, MPI, NCCL, Congestion Control Algorithms)
Demonstrated Performance Analysis skills and methodologies
Experience with NVIDIA GPUs, CUDA library, deep learning frameworks like TensorFlow or PyTorch, combined with expertise in networking collective communication libraries (such as NCCL) and protocols (such as RoCE and RDMA)
Fast and self-learning capabilities with strong analytical and problem-solving skills
Programming Languages: Python, Bash and C languages
Experience with Linux OS distros
Great teammate with good communication and interpersonal skills

Nice to haves:

In-depth knowledge and experience with AI workloads and benchmarking for distributed LLM training
Knowledge in CUDA, and NCCL libraries
Knowledge in Congestion Control algorithms
In-depth System knowledge and understanding (Intel / AMD / ARM CPUs, NVIDIA GPUs, HCA, Memory, PCI)
Strong Performance Analysis skills and methodologies using modern tools

Exploring and researching AI workloads and DL models specifically tailored for large-scale deep learning LLM training on NVIDIA supercomputers and distributed systems focusing on high-performance networking and Nvidia Collective Communications Library (NCCL)
Benchmarking, Profiling, and Analyzing the performance to find bottlenecks and identify areas of improvement and optimizations, with a strong emphasis on networking aspects
Implementing performance analysis tools
Collaborating with many teams from hardware to software to provide performance analysis insights
Defining performance test planning , setting performance expectations for new technologies and solutions, and working to reach the performance targets limits