Senior Performance Engineer – DGX Cloud

NVIDIA

Senior Performance Engineer analyzing performance of AI workloads across various stacks at NVIDIA. Collaborating with engineers and architects to achieve optimization in DGX Cloud systems.

Posted 7/28/2026full-timeSanta Clara • California, Oregon, Texas, Washington • 🇺🇸 United StatesSenior💰 $224,000 - $431,250 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Expertise in performance engineering and optimization of large-scale AI workloads, with strong programming skills in C++ and Python. Proficient in using deep learning frameworks and GPU computing systems to analyze and enhance system performance.

Highest-signal resume keywords

C++ ProgrammingPython ProgrammingPerformance EngineeringCUDADeep Learning Frameworks

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Performance AnalysisBenchmarkingProfilingOptimizationDistributed SystemsOperating SystemsWorkload CharacterizationData AnalysisAutomation WorkflowsSystem-Level Performance Analysis

Soft Skills

CommunicationCollaborationPrioritizationInfluencing

Tools & Technologies

PyTorchJAX/XLAGPU Computing Systems

Industry Keywords

AI WorkloadsLarge-Scale ClustersDeep LearningPerformance Metrics

Tech Stack

Tools & technologies

Distributed SystemsPythonPyTorch

About the role

Key responsibilities & impact

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, trade-offs, and recommendations clearly to influence system and software design decisions.

Requirements

What you’ll need

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
Experience analyzing large-scale AI clusters or distributed training and inference workloads (Ways to stand out from the crowd).
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.

Benefits

Comp & perks

Eligible for equity and benefits