Senior Deep Learning Frameworks CUDA Software Engineer

at Nvidia

USD 184,000-356,500 per year

SENIOR

✅ On-site

Tech Stack
Tag name is followed by "@" symbol and proficiency level value. About proficiency levels:

1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

AI @ 4 CUDA @ 6 Communication @ 4 Deep Learning @ 4 GPU HPC @ 4 JAX @ 4 LLM @ 4 Performance Analysis PyTorch @ 4 Python @ 6 SGLang @ 4 System Architecture @ 4 vLLM @ 4

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

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 completed degree
Development experience with Deep Learning Frameworks such PyTorch, JAX, and Inference Engines such as TRT-LLM, vLLM, SGLang
Rapid prototyping and development with Python, C++, CUDA or related DSLs
Solid grasp of AI models, parallelisms, and/or compiler technologies (e.g. torch.compile)
Experience conducting performance benchmarking on AI clusters. Familiarity with at least one performance profiler toolchain (PyTorch profiler, NVIDIA Nsight Systems)
Understanding of HPC/AI communication concepts
Good understanding of computer system architecture, HW-SW interactions and operating systems principles (aka systems software fundamentals)
Adaptability and passion to learn new frameworks and tools
Flexibility to work and communicate effectively across different teams and timezones