AI Systems Software Engineering Intern – Summer 2027

NVIDIA is looking for an AI Systems Software Engineering Intern to apply AI agents, machine learning, and performance engineering to compilers and developer tools. You will work with compiler and AI engineers on a scoped project involving code, compiler and performance feedback, experiments, and optimization. Depending on your background and interests, the work may focus on agentic workflows, learned models, performance analysis, or AI-enabled developer tools.

What you’ll be doing:

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

What we need to see:

• Pursuing a BS, MS, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related 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 these areas: AI, machine learning, AI agents, compilers, programming languages, systems, performance engineering, optimization, or developer tools.
• Ability to design experiments, analyze results, investigate technical problems, and communicate findings clearly.
• Interest in learning across AI, compiler, performance, and systems boundaries.

Ways to stand out from the crowd:

• 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.
• Familiarity with GPU architecture, CUDA, GPU programming, or performance optimization.
• 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.