Software Engineer Intern, AI and DL Kernel Libraries - 2027

Overview

• NVIDIA is looking for outstanding Software Engineer Interns to help develop groundbreaking technologies for AI and deep learning kernel libraries.
• Our team builds core software that accelerates high-impact AI workloads on NVIDIA GPUs, with a strong focus on deep learning primitives, kernel libraries, and performance-critical GPU software.
• As an intern on the team, you will contribute to the design, development, optimization, and delivery of software that powers NVIDIA's AI platform.
• This internship is centered on foundational library engineering, with opportunities to work on low-level kernels, performance primitives, and efficient implementations for modern AI and deep learning workloads.
• You may contribute to GPU-accelerated deep learning primitives, attention kernel implementations, runtime components, code generation systems, and other performance-critical infrastructure for large language models and advanced AI applications.
• You will collaborate with world-class engineers across deep learning software, compilers, GPU architecture, and open-source inference ecosystems, and your work can directly impact the performance of real-world workloads at scale.
• What you'll be doing Contribute to production-quality software that ships as part of NVIDIA's AI software stack, including cuDNN, FlashInfer, and optimized support for large language model inference workloads.
• Help develop new AI systems technologies for efficient inference, with a focus on performance, scalability, maintainability, and usability.
• 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.