Senior Data Center Performance Engineer - Benchmarking and Optimization

NVIDIA is a technology company advancing accelerated computing, artificial intelligence, graphics, and data center platforms. The Senior Data Center Performance Engineer will lead benchmarking and optimization efforts, characterize AI and HPC workloads, develop performance monitoring automation, identify system bottlenecks, and drive improvements across data center architectures.

Responsibilities:

• Design and execute comprehensive performance benchmarking strategies for our data center platforms and products
• Characterize real-world AI training, inference, and HPC workloads at scale
• Define, track, and report key performance indicators (throughput, latency, efficiency, scaling)
• Build automation tools and frameworks for performance monitoring and analysis
• Identify and analyze performance bottlenecks across compute, memory, network and storage subsystems
• Work closely with architecture, hardware, software, networking, storage and customer teams to resolve performance issues
• Drive performance improvements through system tuning, configuration optimization, and architectural recommendations for future generation systems

Requirements:

• M.S. or Ph.D. in Computer Science, Electrical Engineering or related field (or equivalent experience)
• 8+ years of experience in performance engineering or system architecture
• Deep understanding of computer architecture, hardware-software interaction and computing at-scale
• Strong proficiency in performance profiling tools (Linux perf, NVIDIA Nsight Systems)
• Familiarity with GPU computing and parallel programming (CUDA)
• Background with HPC networking technologies (InfiniBand, RoCE, NVLink)
• Programming skills in Python, C++, and shell scripting. Excellent analytical and problem-solving abilities
• Adaptability and passion to learn new technologies
• Ability to communicate effectively and work with cross-functional global teams
• Experience with AI/ML frameworks (PyTorch, TensorFlow, JAX). Knowledge of MPI, collective communications (NCCL), distributed training and inference. Familiarity with NVIDIA DGX, HGX platforms and other data center solutions
• Familiar with containers, cloud provisioning and scheduling tools (Docker, Kubernetes, SLURM). Understanding of storage systems and I/O performance
• Track record of performance optimization in production environment. Experience with AI code generation tools