NVIDIA is looking for a talented Performance Research and Analysis Engineer to join our Performance group. The ideal candidate will profile and analyze AI workloads on large GPUs and CPUs scale clusters for distributed Deep Learning LLM training and inference focusing at the communication patterns, collectives communication, RDMA, networking and system performance. You will work and interact with many types of HW platforms such as HCAs, Switches, CPUs, GPUs, Systems and also with various SW layers and features. You will experience with simulators and developing performance analysis tools and methodologies to dive deeply into the details, understand performance expectation, limitations, and bottlenecks as part of the root cause analysis of these jobs.
What you'll be doing:
Experience and research AI workloads and DL models specifically tailored for large-scale deep learning LLM training on NVIDIA supercomputers with a focus on High-performance networking.
Benchmarking, Profiling, and Analyzing the performance to find bottlenecks and identify areas of improvement and optimizations, with a strong emphasis on networking aspects.
Implement performance analysis tools.
Collaborating with many teams from HW to SW to provide performance analysis insights.
Define performance test planning, set performance expectations for new technologies and solutions, and work to reach the performance targets limits.
What we need to see:
B.Sc in Computer Science or Software Engineering
8+ years of experience with high-performance Networking (RDMA, MPI, NCCL)
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
Team player with good communication and interpersonal skills
Ways to stand out from the crowd:
In-depth knowledge and experience with AI workloads benchmarking for distributed LLM training, CUDA, and NCCL libraries.
In-depth System knowledge and understanding (Intel / AMD / ARM CPUs, NVIDIA GPUs, HCA, Memory, PCI)
Knowledge in Congestion Control algorithms
NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
