Senior Performance Engineer - Dgx Cloud

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA +4 · Remote

Senior Performance Engineer for NVIDIA's DGX Cloud AI Efficiency Team, focusing on optimizing the performance, efficiency, and resiliency of large-scale AI workloads across compute, network, storage, and software stacks. The role involves analyzing workloads, diagnosing bottlenecks, and driving optimizations in collaboration with deep learning engineers and platform teams.

What you'd actually do

  1. Analyze end-to-end performance of large-scale AI workloads across compute, network, storage, and software stacks.
  2. Design and execute rigorous performance studies to establish baselines, diagnose regressions, and quantify bottlenecks.
  3. Define performance and efficiency evaluation methodologies, benchmarks, and success metrics for AI workloads.
  4. Use profiling, observability, and data analysis to turn performance measurements into actionable optimization plans.
  5. Partner with deep learning engineers, platform teams, and GPU architects to validate and deliver performance improvements.

Skills

Required

  • BS or higher degree in computer science, computer engineering, or a related field (or equivalent experience).
  • 12+ years of experience in strong programming skills in C++ and Python, with the ability to build reliable analysis and automation workflows
  • Solid foundation in operating systems, computer architecture, and distributed systems
  • Experience with performance engineering, benchmarking, profiling, and optimization of complex software or systems
  • Ability to communicate technical findings, prioritize high-impact work, and build alignment across teams

Nice to have

  • Experience analyzing large-scale AI clusters or distributed training and inference workloads
  • Experience with CUDA, GPU computing systems, and GPU performance analysis
  • Hands-on experience with deep learning frameworks such as PyTorch or JAX/XLA
  • Deep understanding of system-level performance analysis, workload characterization, and optimization

What the JD emphasized

  • performance engineering
  • benchmarking
  • profiling
  • optimization
  • large-scale AI clusters
  • distributed training and inference workloads
  • CUDA
  • GPU computing systems
  • GPU performance analysis
  • deep learning frameworks
  • system-level performance analysis
  • workload characterization

Other signals

  • performance optimization
  • large-scale AI workloads
  • GPU systems