About The Position
Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the DL Kernel Library team and see how you can make a lasting impact on the world. We're looking for Software Engineer Interns to help develop groundbreaking technologies in the deep learning inference and training systems software stack! We build innovative AI systems software, primarily cuDNN and FlashInfer. As a member of the team, you'll develop libraries, code generators, and GPU kernel technologies for NVIDIA's hardware architecture. This means designing and building things like new abstractions, efficient kernel implementations, and kernel code generators to accelerate large language models, agents, and other high-impact AI workloads.
Requirements
- BS, MS, or PhD degree in Computer Science, Electrical Engineering, or related field (or equivalent experience)
- Strong experience in developing or using deep learning frameworks (e.g. PyTorch, JAX, TensorFlow, ONNX, etc) or inference engines and runtimes such as vLLM, SGLang, and MLC.
- Strong Python and C/C++ programming skills
Nice To Haves
- Background in domain specific compiler and library solutions for LLM inference and training (e.g. FlashInfer, Flash Attention)
- Expertise in training frameworks and inference engines
- Expertise in machine learning compilers (e.g. Apache TVM, MLIR)
- Strong experience in GPU kernel development and performance optimizations (especially using CUDA C/C++, CUTLASS DSL, cuTile, Triton, or similar)
- Open source project ownership or contributions
Responsibilities
- Innovating and developing new AI systems technologies for efficient inference and training
- Designing, implementing, and optimizing kernels for high impact AI workloads
- Designing and implementing extensible abstractions for serving engines and training frameworks
- Building efficient just-in-time domain specific compilers and runtimes
- Collaborating closely with other engineers at NVIDIA across deep learning frameworks, libraries, kernels, and GPU arch teams
- Contributing to open source communities across FlashInfer, cuDNN, vLLM, SGLang, PyTorch, JAX, and XLA
Benefits
- equity
- benefits
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
