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Principal Software Engineer, E2E Performance and Goodput — CSP Engagements at NVIDIA, US CA Santa Clara

Principal Software Engineer, E2E Performance and Goodput — CSP Engagements at NVIDIA in US CA Santa Clara. Full-time. $272,000 to $431,250 a year. Apply with swipeandhired.

We're looking for a Principal Engineer to join our CSP Engagements team as the technical focal point for end-to-end performance, working directly with engineering teams of key CSP/hyperscale customers to ensure they achieve various performance targets on NVIDIA platforms. In this role, you will augment NVIDIA's performance and benchmark teams with a dedicated CSP-facing focus. You will drive work streams with CSP engineering teams to build shared understanding of platform performance characteristics, gather and incorporate their workload-specific feedback into NVIDIA's optimization priorities, and validate that performance targets are met in customer-representative configurations. Your cross-CSP visibility enables you to identify patterns and drive systemic improvements in documentation, configuration guidance, and tooling.

What you'll be doing:

Drive performance characterization work streams with engineering teams of key CSP/hyperscale customers — ensuring they understand platform performance expectations, profiling methodology, and tuning options for their specific workloads

Gather and synthesize CSP performance feedback — identify gaps between expected and actual throughput, and champion optimization priorities back into NVIDIA's CUDA, NCCL, driver, and firmware teams

Ensure key open-source performance and stress tools (e.g., STREAM, GPU Burn, GPU BLAST) are updated and validated for the latest NVIDIA rack-scale systems, GPU architectures, and CPU platforms — so customers and internal teams have reliable baseline measurements from day one

Work closely with CSPs to ensure their own performance and validation tooling reflects the latest GPU capabilities, memory hierarchy changes, and platform-specific tuning parameters

Conduct cross-CSP performance comparison and pattern analysis — identify configuration, software, or workload differences that explain performance gaps between deployments

Collaborate with CSPs to ensure performance-related integration work (profiling infrastructure, benchmark harnesses, config validation) is ready ahead of deployment milestones

Define test strategies and tooling requirements for performance validation — both for NVIDIA internal certification and customer acceptance

What we need to see:

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: nsight systems, nsight compute, DCGM metrics, or equivalent instrumentation

Understanding of distributed training performance dynamics: computation/communication overlap, pipeline bubbles, memory bandwidth utilization, collective efficiency

Statistical methods for performance analysis: regression detection, confidence intervals, A/B comparison at scale

Understanding of how the full software stack impacts performance: driver overhead, collective algorithm selection, memory allocation, scheduling, firmware power management

Strong data analysis and visualization skills (Python, pandas, dashboards). Customer obsession — genuine passion for understanding why customers aren't achieving expected performance and driving solutions

Ability to communicate performance findings to both deep technical audiences and executive leadership

Demonstrated success influencing multiple engineering teams to prioritize performance improvements

Ways to stand out from the crowd:

Experience profiling and optimizing distributed training at 1000+ GPU scale (Megatron-LM, DeepSpeed, FSDP)

Background in ML infrastructure performance at a CSP/hyperscaler

Familiarity with NVIDIA platforms (DGX, HGX, NVLink topology) and profiling tools

Experience building automated performance regression detection systems for production environments

Understanding of inference workload performance dynamics (vLLM, TensorRT-LLM, SGLang, continuous batching)

NVIDIA is leading 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. We have some of the most forward-thinking and hardworking people on the planet working for us. If you're creative, hardworking and self-motivated, we want to hear from you!

Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary range for this position is 272,000 USD – 431,250 USD.
You will also be eligible for equity and benefits .

Applications for this job will be accepted at least until November 11, 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.