About the Role
NVIDIA is seeking a highly skilled Senior Performance Engineer to join our Performance and R&D organizations. In this role, you will help build and evolve systems that support performance analysis, telemetry, and optimization for large-scale GPU- and CPU-based clusters used in AI and high-performance computing environments. You will work closely with hardware, networking, firmware, and software teams to collect, analyze, and interpret performance data from live systems. This is a fast-paced R&D environment where system behavior and requirements evolve rapidly, requiring adaptable engineering solutions and strong analytical thinking.
Responsibilities
•Profile, benchmark, and analyze AI and HPC workloads on GPU and CPU clusters
•Explore performance characteristics of high-performance networking and collective communications (e.g., NCCL, RDMA, MPI, RoCE)
•Identify performance bottlenecks across networking, compute, memory, and system architecture
•Develop and enhance performance analysis, benchmarking, and diagnostic tools
•Define performance test plans and establish expectations for new technologies and platforms
•Collaborate across hardware, firmware, networking, systems, and software teams to provide actionable performance insights
•Support telemetry collection and data refinement efforts to enable accurate performance analysis
•Maintain high standards for data quality, reproducibility, and traceability of performance results
What We Need to See
•B.Sc. or M.Sc. in Computer Science, Computer Engineering, Software Engineering, or equivalent experience
•5+ years of experience in performance analysis, systems engineering, or HPC/AI infrastructure
•Demonstrated expertise in performance analysis skills and methodologies
•Hands-on experience with high-performance networking (RDMA, MPI, NCCL, congestion control)
•Strong understanding of system performance metrics (latency, throughput, resource utilization)
•Exposure to hardware, firmware, or embedded telemetry environments
•Strong analytical, problem-solving, and communication skills
•Ability to work effectively in cross-functional, fast-paced R&D teams
Requirements
•Knowledge of CUDA, NCCL internals, and congestion control algorithms
•Deep system-level understanding of CPU architectures, GPUs, HCAs, memory, and PCIe
•Experience with NVIDIA GPUs, CUDA, and deep learning frameworks such as PyTorch or TensorFlow
•Experience with cloud platforms
•Proficiency in Python; experience with Bash and C/C++ is a plus
•Strong experience working in Linux environments
