Support the design, implementation, and optimization of kernels for high-impact AI workloads across LLM inference, generative AI, computer vision, autonomous driving, and recommender systems.
Assist in building extensible software abstractions for deep learning libraries, LLM serving engines, and runtime systems.
Contribute to just-in-time compilation, code generation, and runtime technologies for performance-critical GPU workloads.
Analyze workload performance, tune current software, and help propose improvements to future software and hardware-software interfaces.
Collaborate closely with engineers across deep learning frameworks, libraries, kernels, compilers, and GPU architecture teams at NVIDIA.
Contribute to open-source communities and ecosystem integrations where relevant, including projects such as FlashInfer, vLLM, and SGLang.
What we need to see
Currently pursuing a Bachelor's, Master's, or PhD degree in Computer Science, Electrical Engineering, or a related field.
Coursework, research, or hands-on project experience in machine learning, deep learning systems, compilers, systems software, or GPU programming.
Strong programming skills in C/C++ and Python.
Familiarity with CUDA development and GPU programming fundamentals.
Experience developing with or using deep learning frameworks such as PyTorch, JAX, TensorFlow, or ONNX.
Understanding of linear algebra, performance analysis, profiling, and code optimization.
Interest in software abstractions, APIs, and higher-level system architecture for performance-sensitive systems.
Interest in modern machine learning and inference system trends, especially around LLMs and generative AI.
Strong problem-solving skills, curiosity, and the ability to work effectively in a collaborative environment.
Ways to stand out from the crowd
Hands-on experience with inference engines and runtimes such as vLLM, SGLang, MLC, TensorRT-LLM, or similar systems.
Background in domain-specific compilers, code generation, or library solutions for LLM inference and training.
Exposure to machine learning compilers or IR systems such as MLIR, Apache TVM, TensorIR, or related technologies.
Practical experience with GPU performance modeling, computer architecture, or accelerator-oriented software design.
Open-source project ownership or meaningful contributions in deep learning systems, compilers, kernels, or inference infrastructure.
