Senior Systems Software Engineer – GPU Performance

🕒 April 22

🏄 California, North Carolina, +1 more states – Remote

🏄 California – Remote 🌲 North Carolina – Remote 🤠 Texas – Remote

💵 $184k - $356.5k / year

⏰ Full Time

🟠 Senior

🧑‍💻 Full-stack Engineer

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NVIDIA

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1993

🤖 Artificial Intelligence

🎮 Gaming

Artificial Intelligence • Gaming • Automotive

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

• Lead the implementation of performance practices in large-scale GPU infrastructure, delivering powerful tools, methodologies, and flows to validate and improve multiple datacenter products concurrently. • Align next-generation AI workloads with next-generation datacenter builds for NVIDIA GPUs, CPUs, and networking hardware. • Engage early with HW/FW/SW/platform internal and customer teams. • Develop engineering solutions that provide continuous insights into the performance of AI workloads in evolving environments, generating swift insights into improvements and regressions. • Decompose high-complexity performance or stability issues into minimal reproduction cases, working towards identifying the root cause. • Participate in collaborations with various SW and FW teams (BMC/SBIOS/OS/drivers, etc.) to develop outstanding methods and tools. • Analyze, debug, and resolve critical firmware and software issues to achieve the highest AI workload performance at scale.

🎯 Requirements

• Proven understanding of accelerated computing software stacks (CUDA). • Experience with modern cloud and container-based enterprise computing architectures, with Slurm preferred. • Strong programming and scripting experience in C/C++/Python/Bash. • Deep expertise in systems architecture and the impact of various components on performance. • Experience with container technology and Linux-based OSes, with Docker preferred. • Experience supporting high-performance computing or deep learning in engineering or academic research communities. • Strong teamwork and communication skills, coupled with results-focused analytical abilities. • BS in Engineering, Mathematics, Physics, or Computer Science (or equivalent experience); MS or PhD desirable with 8+ years of applicable experience.

🏖️ Benefits

• equity • benefits