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Senior Performance Engineer - DGX Cloud

NVIDIA

Senior Performance Engineer - DGX Cloud

full-timePosted: Jul 28, 2026Updated: Aug 27, 2026Santa Clara, CA, US

Job Description

Joining NVIDIA's DGX Cloud AI Efficiency Team means advancing the performance, efficiency, and resiliency of large-scale AI workloads. We help AI researchers and platform teams understand end-to-end behavior across GPUs, networking, storage, and software stacks. We are seeking a Senior Performance Engineer to characterize workloads, establish performance baselines, diagnose bottlenecks, and drive optimizations from investigation through deployment. Your work will shape scalable DGX Cloud systems, turn complex measurements into prioritized engineering decisions, and continuously raise the performance and reliability of AI workloads. Join our technically diverse team of infrastructure experts to unlock more efficient AI at scale.What you'll be doing:Analyze end-to-end performance of large-scale AI workloads across compute, network, storage, and software stacks.Design and execute rigorous performance studies to establish baselines, diagnose regressions, and quantify bottlenecks.Define performance and efficiency evaluation methodologies, benchmarks, and success metrics for AI workloads.Use profiling, observability, and data analysis to turn performance measurements into actionable optimization plans.Partner with deep learning engineers, platform teams, and GPU architects to validate and deliver performance improvements.Communicate performance findings, tradeoffs, and recommendations clearly to influence system and software design decisions.What we need to see: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 workflowsSolid foundation in operating systems, computer architecture, and distributed systemsExperience with performance engineering, benchmarking, profiling, and optimization of complex software or systemsAbility to communicate technical findings, prioritize high-impact work, and build alignment across teamsWays to stand out from the crowd:Experience analyzing large-scale AI clusters or distributed training and inference workloadsExperience with CUDA, GPU computing systems, and GPU performance analysisHands-on experience with deep learning frameworks such as PyTorch or JAX/XLADeep understanding of system-level performance analysis, workload characterization, and optimizationNVIDIA leads the way in groundbreaking developments in Artificial Intelligence, High-Performance Computing, and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. Our work opens up new universes to explore, enables amazing creativity and discovery, and powers what were once science fiction inventions, from artificial intelligence to autonomous cars. NVIDIA is looking for exceptional people like you to help us accelerate the next wave of artificial intelligence.Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 224,000 USD - 356,500 USD for Level 5, and 272,000 USD - 431,250 USD for Level 6.You will also be eligible for equity and benefits.Applications for this job will be accepted at least until August 1, 2026.This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes.NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Locations

  • Santa Clara, CA, US
  • Austin, TX, US
  • Remote, OR, US (Remote)
  • Remote, WA, US (Remote)
  • Redmond, WA, US

Responsibilities

  • Analyze end-to-end performance of large-scale AI workloads across compute, network, storage, and software stacks.
  • Design and execute rigorous performance studies to establish baselines, diagnose regressions, and quantify bottlenecks.
  • Define performance and efficiency evaluation methodologies, benchmarks, and success metrics for AI workloads.
  • Use profiling, observability, and data analysis to turn performance measurements into actionable optimization plans.
  • Partner with deep learning engineers, platform teams, and GPU architects to validate and deliver performance improvements.
  • Communicate performance findings, tradeoffs, and recommendations clearly to influence system and software design decisions.
  • Analyze end-to-end performance of large-scale AI workloads across compute, network, storage, and software stacks.
  • Design and execute rigorous performance studies to establish baselines, diagnose regressions, and quantify bottlenecks.
  • Define performance and efficiency evaluation methodologies, benchmarks, and success metrics for AI workloads.
  • Use profiling, observability, and data analysis to turn performance measurements into actionable optimization plans.
  • Partner with deep learning engineers, platform teams, and GPU architects to validate and deliver performance improvements.
  • Communicate performance findings, tradeoffs, and recommendations clearly to influence system and software design decisions.
  • This posting is for an existing vacancy.
  • NVIDIA uses AI tools in its recruiting processes.

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