Nvidia
Research Engineer (Networking And System Architecture)
Overview
NVIDIA is seeking an extraordinary innovator in networking and system architecture to join our Research team. In this role, you will focus on designing networks optimized for AI systems, as well as leveraging AI and machine learning to advance network architecture and enable intelligent, real-time decision-making for tasks such as control, routing, congestion management, and scheduling.
About Nvidia
NVIDIA pioneered accelerated computing. Today, our AI infrastructure powers global intelligence, transforming every industry.
Requirements & Eligibility
- Pursuing or recently completed a PhD in relevant discipline(s) (CS, CE, EE, Physics, Math) or equivalent experience.
- 2+ years of relevant industrial and academic experience preferred.
- Background and publication record in systems, networking, computer architecture, and/or ML for networking/systems.
- Signals of fit: evidence via publications plus artifacts demonstrating impactful contributions to systems or network design for AI infrastructure.
- Experience with AI/ML methods for systems and networks supporting AI workloads (e.g., PyTorch/TensorFlow/JAX).
- Strong programming and prototyping ability, with experience building research artifacts, simulators, or system prototypes; C++ and Python preferred.
Key Responsibilities
- Develop innovative network architectures, algorithms, and hardware/software co-design approaches for high-performance interconnects and large-scale distributed AI systems.
- Create and evaluate mechanisms for network and system decision-making in large-scale GPU/accelerator clusters.
- Invent new techniques, technologies, methodologies, processes, and devices, to enable new products or types of products.
- Prototype new ideas in simulation or through analytical modeling.
- Produce technology vision and the basis for products 5-10 years out.
- Participate in the broader research community by serving as a reviewer or on Program Committees, publishing papers and speaking at conferences.
- Collaborate with external researchers, primarily in academia, to encourage mutually beneficial work.
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