Principal Software Engineer, E2E Performance and Goodput - CSP Engagements

NVIDIA Gruppe; Santa Clara (CA); On-site; USD 272,000 - 431,250; Full time

Benefits offered by this job

Equity

Comprehensive benefits

Job summary

NVIDIA Gruppe is looking for a Principal Engineer in Santa Clara, California, to lead performance characterization and work with hyperscale customers. You will play a key role in enhancing NVIDIA’s performance and benchmark teams through collaboration and feedback integration to optimize platform capabilities.

The ideal candidate will have significant expertise in systems performance engineering, proficiency in GPU workload profiling, and the capability to communicate effectively with both technical and executive teams. A competitive salary range of $272,000 to $431,250 USD is offered, along with equity and benefits.

Qualifications

• 15+ years of experience in systems performance engineering, ideally in GPU/HPC/ML infrastructure.
• Understanding of distributed training performance dynamics.
• Proficiency in statistical methods for performance analysis.

Responsibilities

• Drive performance characterization work streams with engineering teams.
• Gather and synthesize CSP performance feedback.
• Ensure open-source performance tools are updated and validated.

Skills

GPU workload profiling

Data analysis and visualization skills (Python, pandas)

Communication with technical and executive audiences

Statistical methods for performance analysis

Education

BS or MS in Computer Science, Computer Engineering, or related field

Tools

Nsight systems

DCGM metrics

Job description

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 (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)

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range 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 June 30, 2026.

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.