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.
