Principal Software Engineer, E2E Performance and Goodput — CSP Engagements

2100 NVIDIA USA; Santa Clara (CA); On-site; USD 272,000 - 431,250; Full time

Benefits offered by this job

Equity opportunities

Comprehensive benefits package

Job summary

NVIDIA Corporation is searching for a Principal Engineer for their CSP Engagements team, focusing on enhancing performance for key consumers of NVIDIA technology. The position demands extensive experience in GPU performance engineering and the ability to communicate effectively with both technical teams and leadership.

Your insights will drive better platform configurations and workload analysis, ensuring that our customers achieve optimal performance expectations. The role requires a strong data analysis background and customer-oriented mindset.

Qualifications

• 15+ years of experience in systems performance engineering.
• Experience with distributed training performance dynamics.
• Ability to communicate performance findings to technical and executive audiences.

Responsibilities

• Lead performance characterization work streams with engineering teams.
• Gather and synthesize CSP performance feedback.
• Ensure tools are updated and validated for NVIDIA systems.

Skills

Systems performance engineering

GPU workload profiling

Data analysis

Customer obsession

Statistical methods

Strong communication skills

Education

BS or MS in Computer Science or related field

Tools

Python

nsight systems

DCGM metrics

Job description

Overview

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

• Lead 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.

Qualifications

• 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

• 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).

Compensation & Benefits

Base salary range: $272,000 USD - $431,250 USD, based on location, experience, and comparable roles. Eligible for equity and benefits.

Equal Opportunity Employer

NVIDIA is committed to fostering an inclusive work environment and is proud to be an equal opportunity employer. NVIDIA does not discriminate 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.