Senior Performance Engineer

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