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.
What you’ll be doing:
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
Ways to stand out from the crowd:
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 as well as a strong experience working in Linux environments