AI Compiler and Kernel Intern – Summer 2027

NVIDIA•Austin, TX

About The Position

NVIDIA is seeking an AI Compiler and Kernel Intern to apply AI agents, machine learning, and performance engineering to compilers, GPU kernels, and developer tools. The intern will work with compiler, kernel, and AI engineers on a scoped project involving code, compiler and kernel feedback, performance experiments, and optimization. Depending on their background and interests, the project may focus on agentic workflows, learned models, compiler optimization, GPU kernel development, performance analysis, or AI-enabled developer tools. Candidates may come from either a compiler or kernel background; experience in every area is not expected.

Requirements

Pursuing a BS, MS, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or a similar field.
Strong programming skills in Python, C, C++, or a similar language.
Solid foundations in data structures, algorithms, software engineering, and quantitative problem solving.
Coursework, research, or project experience in one or more of AI, machine learning, AI agents, compilers, programming languages, GPU kernels, CUDA or accelerator programming, systems, performance engineering, optimization, or developer tools.
Ability to design experiments, analyze results, investigate technical problems, and communicate findings clearly.
Experience in one or more of AI, compiler, kernel, performance, or systems areas, with the ability to learn across technical boundaries.

Nice To Haves

Experience building AI agents, LLM applications, evaluation systems, coding workflows, or developer tools.
Experience with LLVM, MLIR, compiler optimization passes, code generation, domain-specific languages, or compiler infrastructure.
Experience writing, analyzing, or optimizing GPU kernels using CUDA, Triton, or a similar accelerator programming model.
Hands-on experience with reinforcement learning, evolutionary algorithms, predictive modeling, ranking, search, or high-dimensional data analysis.
Research, internships, open-source contributions, or personal projects that measure runtime, compile time, resource use, code quality, or developer productivity.

Responsibilities

Build AI-agent workflows that interact with code, compilers, GPU kernels, benchmarks, performance tools, and engineering systems.
Develop tools that help agents collect data, run experiments, analyze compiler, kernel, and performance feedback, identify optimization opportunities, and recommend next steps.
Explore data-driven approaches for compiler optimization, GPU kernel generation and tuning, performance prediction, decision support, and workflow automation.
Build reliable interfaces between AI agents and compiler, kernel, or developer-tool infrastructure.
Prototype improvements to compiler flows, code generation, GPU kernels, optimization tools, experimentation systems, or developer workflows.
Work with compiler, kernel, architecture, AI, and performance engineers to evaluate ideas on representative workloads.
Measure correctness, runtime performance, compile time, resource utilization, reliability, and other relevant outcomes.
Document the design, experiments, results, limitations, and next steps.