Software Advanced Development Engineer

The NVIDIA Networking Advanced Development Software team develops new groundbreaking technologies to enable new market shares for the company and tighten customer relationships. These are emerging technologies in networking and distributed computing for the booming AI factories and data centers. They span areas such as AI neural networks, Deep Learning, High Performance Computing (HPC), Storage, Cloud, SW Defined Network, Network Function Virtualization and more. We develop the solutions top-down, all the way from application behavioral analysis, to architecture definition and down to the implementation, using the world-leading NVIDIA devices. The development traverses any needed component - application SW, middleware SW, OS kernel subsystems, device drivers, embedded SW (Firmware) and CUDA GPU. We collaborate with partners and key customers in the analysis processes and engage with open source communities introducing our leading features.

What You’ll Be Doing

• Design and implement solutions throughout all layers from high level application, OS and driver subsystem to firmware
• Work on impactful projects involving state-of-the-art high-performance computing hardware and software
• Provide insight and technical guidance and collaborate with peers from across the company - including software architecture, chip architecture, and engineering departments to improve our future technology
• Collaborate with NVIDIA partners and customers
  
  

• B.Sc. in Computer Science, Electrical Engineering, Computer Engineering, or a related field
• 3+ overall years of industry experience in system programming or related fields.
• Understanding of multi core hardware, operating systems design, concurrency, virtual memory, caching, interrupts, device drivers, real-time
• Excellent programming skills
• Ability to learn complex concepts in a fast pace environment.
• A teammate with a can-do attitude, high energy and excellent interpersonal skills
  
  

• Familiarity with networking protocols
• Hands-on experience with CUDA programming and GPU acceleration
• Hands-on experience with LLM serving frameworks
• Experience with open-source projects (coursework, personal, or contributions)
• Working in a fast-paced and dynamic environment