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.
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.Skills Required
- 15+ years of experience in systems performance engineering, ideally in GPU, HPC, or ML infrastructure
- Bachelor’s or master’s degree in Computer Science, Computer Engineering, or a 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 and communication overlap, pipeline bubbles, memory bandwidth utilization, and collective efficiency
- Experience applying statistical methods for performance analysis, including regression detection, confidence intervals, and large-scale A/B comparison
- Understanding of full-stack performance factors, including driver overhead, collective algorithm selection, memory allocation, scheduling, and firmware power management
- Strong data analysis and visualization skills using Python, pandas, and dashboards
- Ability to communicate performance findings to technical audiences and executive leadership
- Demonstrated success influencing multiple engineering teams to prioritize performance improvements
- Experience profiling and optimizing distributed training at 1,000+ GPU scale using Megatron-LM, DeepSpeed, or FSDP
- Background in ML infrastructure performance at a CSP or hyperscaler
- Familiarity with NVIDIA platforms, including DGX, HGX, and NVLink topology, and profiling tools
- Experience building automated performance regression detection systems for production environments
- Understanding of inference workload performance dynamics using vLLM, TensorRT-LLM, or SGLang, including continuous batching
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
-
Equity Value & Accessibility — Feedback suggests equity grants, stock options/RSUs, and a discounted ESPP materially boost total compensation and can deliver significant wealth-building when the stock performs well.
-
Healthcare Strength — Feedback suggests medical, dental, and vision plans with in-network preventive care at 100%, telehealth, and concierge-style support are comprehensive and valued.
-
Parental & Family Support — Feedback suggests up to 22 weeks fully paid parental leave, fertility coverage, and adoption/surrogacy reimbursement provide strong family-focused benefits.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
