Senior Performance Engineer - DGX Cloud
About The Position
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.
Requirements
- 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 Haves
- 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
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.
Benefits
- equity
- benefits
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What This Job Offers
Job Type
Full-time
Career Level
Senior
