Software Engineering Intern, Compiler Verification - Spring 2027

Overview

• At present, NVIDIA is exploring the immense possibilities of AI to define the forthcoming era of computing.
• Our division develops compiler technologies within the CUDA software stack that support the transformation of high-level parallel programs into efficient performance on NVIDIA GPUs.
• This internship grants a hands-on experience working closely with compilers, GPU architecture, and parallel programming languages.
• The role is notable for its mix of systems perspective, software knowledge, and significant exposure to authentic compiler issues across advanced GPU platforms.
• What you’ll be doing: Develop programs in PTX, CUDA, C/C++, or low-level GPU languages to validate compiler and evaluate generated code across key features and architectures.
• Build software components, libraries, and technical solutions that advance compiler development and open up more innovative ways to explore and exercise compiler behavior.
• Collaborate with compiler and hardware engineers on challenging architectural and compiler interactions, helping uncover deeper insights into how advanced GPU software features behave across releases.
• Contribute to expansive engineering projects that improve the robustness, scalability, and future-readiness of the CUDA software stack for new GPU platforms.
• What we need to see: Pursuing an MS or PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field.
• Strong programming skills in Python, C++, or similar languages used for systems or software development.
• Solid understanding of data structures, algorithms, and software debugging fundamentals.
• Familiarity with compiler concepts, computer architecture, operating systems, or parallel programming.
• Ability to read technical details carefully, reason about complex behaviors, and communicate findings clearly in a collaborative engineering environment.
• Ways to stand out from the crowd: Exposure to compiler internals, code generation, static analysis, LLVM, or MLIR.
• Background in GPU programming, CUDA, PTX, or parallel computing models.
• Hands-on project work involving low-level systems, program analysis, or software aimed at optimizing software operation.
• Passion for compilers, GPU frameworks, or software tools intended for developer use.