Principal Software Engineer – E2E Performance, Goodput

NVIDIA

. Drive performance characterization work streams with engineering teams of key CSP/hyperscale customers, ensuring understanding of platform performance expectations, profiling methodology, and tuning options for specific workloads .

Posted 11/7/2026full-timeRemote • United StatesLead💰 $272,000 - $431,250 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive expertise in GPU workload profiling and performance optimization, with a strong focus on statistical analysis and data visualization. Proven ability to collaborate with engineering teams and communicate complex performance findings effectively to diverse audiences.

Highest-signal resume keywords

GPU Workload ProfilingPerformance AnalysisData Visualization SkillsDistributed Training OptimizationNVIDIA Platform Familiarity

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Nsight SystemsNsight ComputeDCGM MetricsStatistical MethodsPerformance Regression DetectionInference Workload DynamicsMemory Bandwidth UtilizationCollective EfficiencyBenchmark HarnessesConfiguration Validation

Soft Skills

Communication SkillsInfluencing Engineering Teams

Tools & Technologies

STREAMGPU BurnGPU BLASTPythonPandasDashboardsMegatron-LMDeepSpeedFSDPTensorRT-LLM

Industry Keywords

CSPHyperscaleHPCML InfrastructureNVIDIA PlatformsPerformance CharacterizationOpen-Source ToolsRack-Scale SystemsDriver OverheadFirmware Power Management

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 understanding of platform performance expectations, profiling methodology, and tuning options for specific workloads
Gather and synthesize CSP performance feedback, identify gaps between expected and actual throughput, and champion optimization priorities to NVIDIA's CUDA, NCCL, driver, and firmware teams
Ensure open-source performance and stress tools such as STREAM, GPU Burn, and GPU BLAST are updated and validated for the latest NVIDIA rack-scale systems, GPU architectures, and CPU platforms
Work with CSPs to ensure their 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 to identify configuration, software, or workload differences explaining deployment performance gaps
Collaborate with CSPs to prepare profiling infrastructure, benchmark harnesses, and configuration validation for deployment milestones
Define test strategies and tooling requirements for performance validation 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)
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)

Benefits

Comp & perks

Equity
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