CUDA Deep Learning Systems Architect

NVIDIA; Austin (TX); On-site; USD 124,000 - 196,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA in Austin, TX seeks an experienced software professional to advance CUDA and Deep Learning systems, exploring the intersection of DL models and low-level hardware optimization. You will push performance from single GPUs to cluster-scale environments, delivering kernels, profiling tools, and scalable runtimes.

Join a highly technical, research-oriented team focused on model optimization, accelerator constraints, and novel workloads, with opportunity for equity and benefits.

Qualifications

• BS, MS, or PhD in CS/CE/EE or related field (or equivalent experience).
• 2+ years of relevant industry experience or equivalent 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

• Explore, research, and prototype novel systems optimizations for advanced 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 seamlessly from a single node to massive, 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, memory bandwidth, cross-node network communication efficiency and programmability.
• 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, upstream framework integrations, internal tools, or closed-source commercial products.

Skills

C++

Python

CUDA

Deep Learning

Distributed Computing

Performance Optimization

Research

Education

BS/MS/PhD in CS or related

Job description

NVIDIA in Austin, TX seeks an experienced software professional to advance CUDA and Deep Learning systems, exploring the intersection of DL models and low-level hardware optimization. You will push performance from single GPUs to cluster-scale environments, delivering kernels, profiling tools, and scalable runtimes.

Join a highly technical, research-oriented team focused on model optimization, accelerator constraints, and novel workloads, with opportunity for equity and benefits.