Principal Software Engineer, E2E Performance And Goodput — CSP Engagements
Tech Stack
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
CUDA
Communication @ 4
Data Analysis @ 7
GPU @ 8
HPC @ 8
LLM @ 4
Leadership @ 6
Machine Learning
NCCL
NVLink @ 3
Pandas @ 7
Performance Analysis @ 6
Profiling @ 4
Python @ 7
SGLang @ 4
TensorRT @ 4
vLLM @ 4
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
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.
Responsibilities
- 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
Requirements
- 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.