Principal Software Engineer – E2E Performance, Goodput

NVIDIA

Principal Software Engineer focusing on end-to-end performance for CSP Engagements team at NVIDIA. Collaborating with engineering teams to achieve performance targets on NVIDIA platforms.

Posted 6/27/2026full-timeSanta Clara • California, Oregon, Texas • 🇺🇸 United StatesLead💰 $272,000 - $431,250 per yearWebsite

Tech Stack

Tools & technologies

PandasPython

About the role

Key responsibilities & impact

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

What you’ll need

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

Benefits

Comp & perks

equity
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Hard Skills & Tools

Performance CharacterizationGPU Workload ProfilingStatistical Methods for Performance AnalysisUnderstanding of Distributed Training DynamicsData AnalysisPerformance ValidationTest Strategy DefinitionPerformance OptimizationBenchmarkingConfiguration Validation

Soft Skills

Customer ObsessionCommunication SkillsInfluencing SkillsCollaborationProblem-SolvingAnalytical ThinkingLeadershipPassion for Performance Improvement