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About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
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The Principal Software Engineer, E2E Performance and Goodput — CSP Engagements joins the CSP Engagements team as a technical focal point for end-to-end performance. This role involves driving performance characterization work streams with engineering teams of key CSP and hyperscale customers to ensure they achieve specific performance targets on NVIDIA platforms. Responsibilities include gathering workload-specific feedback to influence optimization priorities for CUDA, NCCL, driver, and firmware teams, while validating open-source tools like STREAM, GPU Burn, and GPU BLAST. The position requires proficiency in GPU workload profiling using Nsight Systems, Nsight Compute, and DCGM metrics, alongside knowledge of distributed training dynamics and statistical methods. Candidates must utilize Python and pandas for data analysis to solve complex performance gaps across the full software stack.
What does a Software Engineer earn in California?
Median $214500 from 1193 postings across 76 companies.
Drive performance characterization work streams with CSP engineering teams to ensure they understand platform expectations and tuning options.
Synthesize customer performance feedback to champion optimization priorities for CUDA, NCCL, driver, and firmware teams.
Update and validate open-source performance and stress tools for the latest NVIDIA rack-scale systems and GPU architectures.
Ensure customer performance and validation tooling reflects the latest GPU capabilities and platform-specific tuning parameters.
Conduct cross-CSP performance comparison and pattern analysis to identify configuration or software differences causing performance gaps.
Manage performance-related integration work including profiling infrastructure and benchmark harnesses ahead of deployment milestones.
Define test strategies and tooling requirements for performance validation for internal certification and customer acceptance.
What we're looking for
15+ years of experience in systems performance engineering, ideally in GPU/HPC/ML infrastructure.
BS or MS in Computer Science, Computer Engineering, or related field (or equivalent experience).
Proficiency in GPU workload profiling using nsight systems, nsight compute, DCGM metrics, or equivalent instrumentation.
Understanding of distributed training performance dynamics, including computation/communication overlap, pipeline bubbles, and memory bandwidth utilization.
Knowledge of how the full software stack impacts performance, including driver overhead, collective algorithms, and firmware power management.
Strong data analysis and visualization skills using Python, pandas, and dashboards.
Ability to communicate performance findings to both deep technical audiences and executive leadership.
Experience profiling and optimizing distributed training at 1000+ GPU scale (preferred).
System Software Architecture
Linux Kernel
Device Drivers
x86
ARM
aarch64
CUDA
GPU Computing
CXL
Computer Architecture
Performance Analysis
Deep Learning
High-Performance Computing
Memory Fabric
In-band Management