Nvidia Senior Technical Marketing Engineer - AI Infrastructure 

United States, California 

522074571

What you will be doing:

• Evaluate and run multi-node jobs on large clusters to assess performance and developer experience in distributed deep learning environments.

• Understand and profile workloads for deep learning applications. Educate customers and tech press to run these workloads and benchmark NVIDIA data center systems for performance evaluations.

• Conduct performance benchmarking of AI infrastructure with industry-standard models and frameworks (e.g., vLLM, PyTorch, TensorFlow) to measure throughput, latency, and scalability.

• Engage with various teams across NVIDIA such as product, marketing, hardware, software engineering, and QA to improve NVIDIA's product offerings.

• Develop developer-focused content, including detailed tutorials and code samples, to demonstrate the latest features in NVIDIA’s tools and libraries.

• Write technical whitepapers, product briefs, and solution blueprints to highlight innovative use cases, architecture designs, and best practices in AI infrastructure.

• Deliver live demos and technical presentations at leading industry events, such as the NVIDIA GPU Technology Conference (GTC), CES, SIGGRAPH, and other global conferences, showcasing cutting-edge NVIDIA technologies.

• A Bachelor’s or Master’s in Software Engineering, Computer Science, Computer Engineering, Electrical Engineering or equivalent experience.

• 5+ years of experience.

• Proficiency in Python and C++ for AI and HPC applications.

• Experience using large scale multi node GPU infrastructure on premise or in CSPs

• Background in deep learning model architectures and experience with Pytorch and large scale distributed training

• Deep understanding of computer architecture, and familiarity with the fundamentals of GPU architecture. Experience with NVIDIA's Infrastructure and software stacks.

• Demonstrate proficiency in managing job scheduling, workload orchestration, and deploying multi-node GPU clusters using Slurm and Kubernetes.

• Solid understanding of network protocols, distributed system communication, and high-speed interconnects (e.g., InfiniBand, RDMA, Ethernet) to optimize data flow across nodes in HPC environments.

• Hands-on experience with NVIDIA GPUs, HPC storage, networking, and cloud computing.