Senior Firmware 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.

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  Profile, benchmark, and analyze AI and HPC workloads on GPU and CPU clusters

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  Explore performance characteristics of high-performance networking and collective communications (e.g., NCCL, RDMA, MPI, RoCE)

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  Identify performance bottlenecks across networking, compute, memory, and system architecture

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  Develop and enhance performance analysis, benchmarking, and diagnostic tools

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  Define performance test plans and establish expectations for new technologies and platforms

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  Collaborate across hardware, firmware, networking, systems, and software teams to provide actionable performance insights

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  Support telemetry collection and data refinement efforts to enable accurate performance analysis

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  Maintain high standards for data quality, reproducibility, and traceability of performance results

• 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

• 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