Principal Software Engineer, E2E Performance and Goodput — CSP Engagements

NVIDIA is leading the way in groundbreaking developments in Artificial Intelligence, High-Performance Computing and Visualization. They are seeking a Principal Engineer to join their CSP Engagements team, focusing on end-to-end performance and working with key CSP/hyperscale customers to achieve performance targets on NVIDIA platforms.

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
• 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)