Principal Software Engineer – E2E Performance, Goodput

Job not on LinkedIn

🔥 10 minutes ago

🏄 California, Oregon, +1 more states – Remote

🏄 California – Remote 🌲 Oregon – Remote 🤠 Texas – Remote

💵 $272k - $431.3k / year

⏰ Full Time

🔴 Lead

🧑‍💻 Full-stack Engineer

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NVIDIA

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1993

🏥 Healthcare

🏭 Manufacturing

🤖 Artificial Intelligence

Healthcare • Manufacturing • Artificial Intelligence

NVIDIA is a leading technology company specializing in accelerated computing and artificial intelligence. NVIDIA pioneers advancements in graphical processing units (GPUs), cloud computing, data centers, and virtual reality, with a focus on gaming, automotive, healthcare, and robotics industries. The company's innovations, such as NVIDIA Omniverse, transform traditional digital processes by enabling high-fidelity simulations and rendering tasks. Their applications span various industries, from autonomous vehicles using NVIDIA DRIVE to healthcare solutions with NVIDIA Clara, and AI-driven analytics and workflows.

📋 Description

• 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

• 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

• Equity • Benefits

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