Senior Performance Engineer - DGX Cloud

at Nvidia

USD 224,000-431,200 per year

SENIOR

✅ On-site

Tech Stack
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AI @ 4 CUDA @ 4 Data Analysis Deep Learning @ 4 Distributed Systems @ 6 GPU @ 4 JAX @ 4 Networking @ 4 Observability Performance Analysis @ 4 Profiling @ 4 PyTorch @ 4 Python @ 7

Details

Joining NVIDIA's DGX Cloud AI Efficiency Team means advancing the performance, efficiency, and resiliency of large-scale AI workloads. The team helps AI researchers and platform teams understand end-to-end behavior across GPUs, networking, storage, and software stacks. The Senior Performance Engineer will characterize workloads, establish performance baselines, diagnose bottlenecks, and drive optimizations from investigation through deployment. This work will shape scalable DGX Cloud systems, turn complex measurements into prioritized engineering decisions, and continuously improve the performance and reliability of AI workloads.

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.

Requirements

Bachelor's degree or higher in computer science, computer engineering, or a related field, or equivalent experience.
12+ years of experience and 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.

Preferred Qualifications

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.

Benefits

Base salary range of $224,000–$356,500 for Level 5 or $272,000–$431,250 for Level 6, depending on location, experience, and the pay of employees in similar positions.
Eligibility for equity and benefits.
Applications will be accepted at least until August 1, 2026.
NVIDIA is an equal opportunity employer committed to an inclusive work environment.