CUDA Deep Learning Systems Engineer — High-Performance AI

NVIDIA AI; Santa Clara (TX); On-site; USD 124,000 - 196,000; Full time

Benefits offered by this job

Equity

Job summary

NVIDIA in the United States is seeking an experienced software professional to work at the intersection of CUDA and Deep Learning Systems, focusing on hardware-aware optimization and scalable AI workloads from single nodes to clusters.

You will prototype novel optimizations, design high-performance CUDA kernels, and collaborate with AI researchers and kernel engineers to push accelerator compute utilization and memory bandwidth.

Qualifications

• BS, MS, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or related field, or equivalent experience.
• 2+ years of relevant industry or academic experience after degree achievement.
• Strong proficiency in C++ and Python programming.
• Solid background in the fundamentals of Deep Learning with a focus on transformers.
• Strong understanding of distributed computing principles, multi-node scaling, and the unique performance challenges of cluster-scale execution.
• Proven experience in systems programming, computer architecture, and low-level systems performance optimization.
• Familiarity with deep learning accelerator architectures such as the GPU and hands-on experience with CUDA programming, kernel optimization, and workload profiling.
• Experience profiling and optimizing generative AI models, including but not limited to, pioneering large language models.
• Research background in machine learning systems or adjacent fields and experience profiling and optimizing innovative vision models, generative AI architectures, or diffusion models.
• A track-record of initiative and willingness to deep-dive on problems across the stack.

Responsibilities

• Prototype optimizations for deep learning models at the intersection of high-level DL frameworks and low-level CUDA through modeling, simulation, and silicon prototyping.
• Architect and optimize distributed computing systems that scale from a single node to cluster-scale supercomputing environments.
• Design, implement, and optimize custom high-performance CUDA kernels tailored to emerging neural network architectures and workloads.
• Analyze complex hardware-software interactions to identify and resolve performance bottlenecks in both training and inference pipelines.
• Collaborate closely with AI researchers, HW and SW architects, kernel and compiler authors and CUDA driver experts to co-design systems and algorithms that improve accelerator compute utilization and memory bandwidth.
• Develop exploratory tools and runtime systems to profile and accelerate new paradigms in deep learning.
• Write clean, effective, and maintainable code, ensuring exploratory prototypes can smoothly transition into open-source releases or production.

Job description

NVIDIA in the United States is seeking an experienced software professional to work at the intersection of CUDA and Deep Learning Systems, focusing on hardware-aware optimization and scalable AI workloads from single nodes to clusters.

You will prototype novel optimizations, design high-performance CUDA kernels, and collaborate with AI researchers and kernel engineers to push accelerator compute utilization and memory bandwidth.