CUDA Deep Learning Systems Engineer

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

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA in the United States is seeking an experienced software professional to work on pioneering initiatives at the intersection of CUDA and Deep Learning Systems. You will help optimize hardware performance from a single GPU to large clusters and prototype next‑generation ideas bridging DL algorithms with CUDA, including custom kernels and cluster-scale AI systems design.

The role emphasizes collaboration with AI researchers, HW and SW architects, and CUDA experts to improve performance,

Qualifications

• BS, MS, or PhD in Computer Science, Computer Engineering, Electrical Engineering, 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 optimisation.
• Familiarity with deep learning accelerator architectures such as the GPU and hands-on experience with CUDA programming, kernel optimisation, and workload profiling
• Experience profiling and optimising 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 optimising 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 Systems

Performance Profiling

Kernel Optimization

Multi-node Scaling

Education

BS/MS/PhD in CS/CE/EE

Tools

CUDA toolkit

NCCL

MPI

UCX

Triton

Job description

NVIDIA in the United States is seeking an experienced software professional to work on pioneering initiatives at the intersection of CUDA and Deep Learning Systems. You will help optimize hardware performance from a single GPU to large clusters and prototype next‑generation ideas bridging DL algorithms with CUDA, including custom kernels and cluster-scale AI systems design.

The role emphasizes collaboration with AI researchers, HW and SW architects, and CUDA experts to improve performance,