Systems Software Engineer, Kubernetes Scale

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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

• Drive end-to-end performance and scale characterization for the NVIDIA DGX Cloud software stack • Collaborate with AI researchers, developers and customers to develop innovative, automated tests • Deep dive into performance and scale issues in complex distributed systems • Design and develop monitoring, reporting and analysis tools for performance and scale testing • Triage, debug and root cause issues related to operating Kubernetes clusters at ultra-large scale • Build and maintain a high-velocity framework that enables continuous performance and scale testing • Document research, methodologies and results clearly and concisely • Engage efficiently with upstream communities

🎯 Requirements

• 2+ years of experience • Computer Architecture, Networking, Storage systems, Accelerators • Bachelors/Masters in Engineering (preferably, Electrical Engineering, Computer Engineering, or Computer Science) or equivalent experience • Expertise in Kubernetes and familiarity with related CNCF projects • Background in working with large scale parallel and distributed accelerator-based systems • Expertise optimizing performance and AI workloads on large scale systems • Experience with performance modeling and benchmarking at scale • Proficiency in Golang/Python • Background with the NVIDIA software ecosystem in both training and inference domains • Expertise with at least one of public CSP infrastructure (GCP, AWS, Azure, OCI for example)