Senior Software Engineer, CUDA Deep Learning Systems

NVIDIA; United States; On-site; USD 184,000 - 357,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA is seeking an experienced software professional to work on CUDA and Deep Learning Systems at the intersection of high performance computing and AI workloads.

You will help design and optimize distributed systems, write high-performance CUDA kernels, and collaborate with AI researchers and HW/SW architects to push accelerator compute utilization and memory bandwidth. The role spans training and inference workloads, from a single GPU to large clusters.

Qualifications

• BS, MS, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or related field (or equivalent experience).
• 8+ years of relevant industry or academic experience after degree.
• Strong proficiency in C++ and Python.
• Solid background in Deep Learning with focus on transformers.
• Understanding of distributed computing, multi-node scaling, and cluster execution challenges.
• Experience in systems programming, computer architecture, and low-level optimization.
• Familiarity with GPU accelerator architectures, CUDA programming, kernel optimization, and workload profiling.
• Experience profiling and optimizing generative AI models and vision/diffusion models.
• Research background in ML systems and ability to tackle cross-stack problems.

Responsibilities

• Explore, research, and prototype novel system optimizations for advanced DL models at the intersection of DL frameworks and CUDA.
• Architect and optimize distributed computing systems from a single node to cluster-scale environments.
• Design, implement, and optimize custom high-performance CUDA kernels for emerging NN architectures.
• Analyze hardware-software interactions to identify and resolve performance bottlenecks in training and inference.
• Collaborate with AI researchers, HW/SW architects, kernel and compiler authors, and CUDA experts to improve utilization, memory bandwidth, and cross-node efficiency.
• Develop exploratory tools and runtimes to profile and accelerate new DL paradigms.
• Write clean, maintainable code enabling prototypes to transition to open-source releases, integrations, or products.

Skills

C++

Python

Deep Learning

Distributed computing

CUDA

Kernel optimization

Profiling

Transformers

Multi-node scaling

Education

BS/MS/PhD in Computer Science/Engineering or related field

Tools

CUDA

NCCL

MPI

UCX

Triton

XLA

torch.compile

Job description

We are looking for an experienced and highly motivated software professional to work on pioneering initiatives and projects at the intersection of CUDA and Deep Learning Systems. As the complexity and scale of artificial intelligence continue to grow, the intersection of advanced deep learning architectures, massive-scale distributed computing, and low-level hardware optimization has never been more critical. Our team is dedicated to exploring and prototyping next-generation ideas that bridge the gap between deep learning algorithms and CUDA, pushing the boundaries of what is possible on modern accelerator architectures.

Join our dynamic, research-oriented team to help unlock maximum hardware performance for emerging AI workloads. You will be a crucial member of a highly technical group exploring uncharted territories in model optimization, custom kernel development, and cluster-scale AI systems design. If you are passionate about the fundamentals of deep learning and thrive on squeezing every ounce of performance out of advanced computing systems from a single GPU to supercomputer clusters, we want you on our team!

What you will be doing:

• 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.

What we need to see:

• BS, MS, or PhD degree in Computer Science, Computer Engineering, Electrical Engineering, or related field (or equivalent experience).
• 8+ 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.

Ways to stand out from the crowd:

• Deep expertise in performance internals and execution graphs of major deep learning training and inference frameworks (e.g., PyTorch, JAX, TensorRT, vLLM, sgLang, Nemo, Megatron).
• Hands-on experience with communication libraries (e.g., NCCL, MPI, UCX) and distributed machine learning techniques (e.g., pipeline, tensor, expert parallelism).
• Knowledge of numerical methods and low-precision arithmetic (e.g., NVFP4, MXFP4, FP8, INT8) and their impact on deep learning accuracy and performance.
• Background in deep learning compilers and ML systems, including graph-level and codegen tools (e.g., Triton, XLA, torch.compile) and highly parallel/RL-style simulation environments.
• Experience designing and implementing agentic AI systems applied to complex systems and infrastructure problems.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.

You will also be eligible for equity and benefits .

Applications for this job will be accepted at least until September 4, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity empl