CUDA-Powered Deep Learning Frameworks Engineer

NVIDIA Corporation; Santa Clara (CA); Hybrid; USD 184,000 - 357,000; Full time

Job summary

NVIDIA Corporation is seeking a motivated Deep Learning engineer to advance CUDA features and Distributed Runtime technologies within AI stacks, including PyTorch, TRT-LLM, vLLM, SGLang, JAX, and more.

You will work on scaling Deep Learning and HPC applications across multi-GPU and multi-node environments, collaborating with researchers and engineers to optimize performance and programmability. Equity and benefits apply.

Qualifications

• 8+ years of relevant industry experience or equivalent academic experience after degree.
• Experience with Deep Learning Frameworks PyTorch, JAX; Inference engines like TRT-LLM, vLLM, SGLang.
• Prototyping and development with Python, C++, CUDA or related DSLs.
• Strong understanding of AI models, parallelism, and compiler tech (torch.compile).
• Experience benchmarking AI clusters and profiling tools (Nsight Systems).
• Familiarity with HPC/AI communications concepts and systems software fundamentals.

Responsibilities

• Integrate new CUDA features and runtime abstractions in AI frameworks from PoC to production.
• Analyze AI workloads to identify improvements in lower stack layers.
• Collaborate with teams on latest AI models and frameworks.
• Own and drive improvements in AI compiler-runtime interfaces for large-scale multi-GPU setups.
• Design fault-tolerant, elastic solutions for dynamic AI workloads.
• Influence CUDA roadmap to enable next-gen DL frameworks.
• Collaborate across time zones with researchers, HW/SW architects, and kernel/driver experts.
• Develop exploratory tools to profile and accelerate new DL paradigms.

Skills

Deep learning expertise

Distributed systems

Performance benchmarking

Cross-team collaboration

Education

BS/MS/PhD in CS/CE/EE or equivalent

Tools

CUDA

PyTorch

JAX

TensorRT

vLLM

SGLang

NVIDIA Nsight Systems

NCCL

MPI

Job description

NVIDIA Corporation is seeking a motivated Deep Learning engineer to advance CUDA features and Distributed Runtime technologies within AI stacks, including PyTorch, TRT-LLM, vLLM, SGLang, JAX, and more.

You will work on scaling Deep Learning and HPC applications across multi-GPU and multi-node environments, collaborating with researchers and engineers to optimize performance and programmability. Equity and benefits apply.