Intelligent machines powered by Artificial Intelligence computers that can learn, reason and interact with people are no longer science fiction. GPU Deep Learning has provided the foundation for machines to learn, perceive, reason and solve problems. Today, visual computing is a crucial tool in helping people get along with technology, and we have extended its technology into datacenters, mobile devices and cars. There has never been a more exciting time to join our team – if this role sounds like a fit for you, we'd love to hear from you
we are seeking a Senior Performance Analysis Engineer to join our Performance group. In this exciting role, you will profile and analyze AI workloads on large GPUs and CPUs scale clusters for distributed Deep Learning LLM training focused on collectives communication and networking. You will interact with many types of hardware and platforms, such as HCAs, Switches, CPUs, GPUs, and Systems. You will develop performance analysis tools and methodologies to dive deeply into the details and understand performance expectations, limitations, and bottlenecks.
What you'll be doing:
Exploring and researching AI workloads and DL models specifically tailored for large-scale deep learning LLM training on our 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.
we are seeking a Senior Performance Analysis Engineer to join our Performance group. In this exciting role, you will profile and analyze AI workloads on large GPUs and CPUs scale clusters for distributed Deep Learning LLM training focused on collectives communication and networking. You will interact with many types of hardware and platforms, such as HCAs, Switches, CPUs, GPUs, and Systems. You will develop performance analysis tools and methodologies to dive deeply into the details and understand performance expectations, limitations, and bottlenecks.
What you'll be doing:
Exploring and researching AI workloads and DL models specifically tailored for large-scale deep learning LLM training on our 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.
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
Ways to stand out from the crowd:
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.
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
Ways to stand out from the crowd:
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.
This position is open to all candidates.
