Senior Deep Learning Frameworks CUDA Software Engineer

NVIDIA is leading the way in groundbreaking developments in Artificial Intelligence, High Performance Computing and Visualization.

The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services.

Our work opens up new universes to explore, enables amazing creativity and discovery, and powers what were once science fiction inventions from artificial intelligence to autonomous cars.

We are looking for a motivated Deep Learning engineer to bring advanced CUDA features and Distributed Runtime technologies into AI stacks, including PyTorch, TRT-LLM, vLLM, SGLang, JAX, etc.

You will be working with the team that created core CUDA features and runtimes for scaling Deep Learning and HPC applications.

Your customers will have diverse multi-GPU demands, ranging from training on scales up to 100K GPUs to inference down at microsecond latency.

CUDA features improve both productivity and performance of AI applications.

Your work in AI toolkits will accelerate enabling those for the community.

This is an outstanding opportunity for someone with an AI background to advance the state of the art in this space.

Are you ready to contribute to the development of innovative technologies and help realize NVIDIA's vision?

What you will be doing:

• Integrate new CUDA features and Runtime abstractions in AI frameworks: from PoC to performance analysis to production
• Perform deep analysis of AI workloads and frameworks to identify requirements and opportunities to innovate in the lower layers of the stack. Collaborate hands-on with teams working on the latest AI models.
• Own and drive improvements in the AI Compiler-Runtime interface to build speed-of-light multi-GPU multi-node solutions.
• Design fault-tolerant and elastic solutions for large-scale or dynamic AI workloads.
• Influence the roadmap of core CUDA to facilitate building next-gen DL frameworks.
• Collaborate with a very dynamic team across multiple time zones.
• Collaborate closely with AI researchers, HW and SW architects, kernel and compiler authors and CUDA driver experts to co-design systems and frameworks that enhance performance 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.