AISoftwareEngineer,KernelLibraries-NewCollegeGrad2026

We're looking for outstanding AI systems engineers to develop groundbreaking technologies in the inference systems software stack! We build innovative AI systems software to accelerate for AI inference. 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 attention kernel implementations, new LLM inference runtimes components, and kernel code generators to accelerate large language models, agents, and other high-impact AI workloads. **What you 'll be doing:** * Innovating and developing new AI systems technologies for efficient inference * Designing, implementing, and optimizing kernels for high impact AI workloads * Designing and implementing extensible abstractions for LLM serving engines * 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 like FlashInfer, vLLM, and SGLang **What we need to see:** * Masters degree in Computer Science, Electrical Engineering, or related field (or equivalent experience); PhD are preferred * 2 + years (academic/ industry) experience with ML/DL systems development preferable * Strong experience in developing or using deep learning frameworks (e.g. PyTorch, JAX, TensorFlow, ONNX, etc) and ideally inference engines and runtimes such as vLLM, SGLang, and MLC. * Strong Python and C/C++ programming skills **Ways to stand out from the crowd:** * Background in domain specific compiler and library solutions for LLM inference and training (e.g. FlashInfer, Flash Attention) * Expertise in inference engines like vLLM and SGLang * Expertise in machine learning compilers (e.g. Apache TVM, MLIR) * Strong experience in GPU kernel development and performance optimizations (especially using CUDA C/C++, cuTile, Trit