Compiler Engineering Intern

At Intel, we are building the next generation of software technologies that unlock the potential of heterogeneous computing. Our compiler technologies help developers harness CPUs, GPUs, and accelerators through high-performance, portable software.

As an intern, you will contribute to an open ecosystem that advances performance, scalability, reliability, and developer productivity for AI, HPC, and other demanding workloads, while supporting Intel's commitment to contributing to and collaborating with open-source communities.

The Intel Compiler Engineering Team develops LLVM-based C, C++, SYCL, Triton, MLIR, Domain Specific Languages (DSLs), and Fortran compiler technologies for Intel processor platforms, including CPUs and GPUs.

The team works across the compiler stack and in close collaboration with hardware architecture teams, covering language front ends, intermediate representations, optimization, code generation, debug information, runtimes, offload support, quality, and developer tooling. This work connects Intel hardware and compiler capabilities with AI frameworks and software ecosystems such as PyTorch, inference engines, and machine-learning models.

You will collaborate with experienced engineers and contribute to open-source communities while learning how production compilers are designed, tested, optimized, and delivered.

We are hiring a Compiler Engineer Intern to contribute to Intel's compiler software stack. You will work with experienced compiler and hardware architecture engineers on a scoped project that may involve:

• Compiler features or MLIR-based intermediate representations
• Triton or other Domain Specific Languages (DSLs)
• Optimization, code generation, runtime, or offload support
• Debug information or testing infrastructure
• Performance analysis or developer tools

Projects may involve connecting software capabilities to AI frameworks such as PyTorch, inference engines such as vLLM and SGLang, and machine-learning models such as DeepSeek, Hugging Face, and Kimi.

You will investigate technical problems, implement and test solutions, analyze results, and communicate findings to the team. This opportunity is designed for an undergraduate student who wants to build practical compiler-engineering experience and apply foundational computer science concepts to production software.

• →Contribute to the design, implementation, testing, or analysis of compiler and runtime technologies
• →Develop and debug software in C or C++ within a large, collaborative codebase
• →Create or improve tests, reproduce defects, analyze failures, and help deliver reliable solutions
• →Evaluate functional correctness, compile-time behavior, runtime performance, or code quality, as applicable to the project
• →Participate in code reviews, technical discussions, and project planning with mentors and team members
• →Document designs, experiments, results, and lessons learned, and present project outcomes to the team
• →Contribute to open-source projects when aligned with the assigned work

You should demonstrate the following professional traits:

• Curiosity and willingness to learn unfamiliar technologies
• Strong analytical, problem-solving, and debugging skills
• Clear written and verbal communication

• Currently pursuing a Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, Applied Mathematics, Informatics, or a related technical discipline
• 3+ months of coursework or project experience in data structures, algorithms, computer architecture, operating systems, programming languages, or compiler construction
• Experience programming in C++ through coursework, research, personal projects, or prior internships
• Experience using software development and debugging tools in a Linux or similar development environment

• Compiler-related coursework or project experience, including parsing, intermediate representations, optimization, code generation, or runtimes
• Hands-on compiler development experience with LLVM, MLIR, Triton, GCC, Clang, or similar compiler frameworks
• Experience with GPU programming models such as CUDA, SYCL, OpenMP, or HIP
• Knowledge of CPU or GPU architecture, heterogeneous computing, or systems programming
• Familiarity with AI frameworks such as PyTorch, inference engines such as vLLM and SGLang, and machine-learning model ecosystems such as DeepSeek, Qwen, and Kimi
• Tile-based programming model knowledge
• GPU compute kernel writing experience
• Experience with Git, automated testing, continuous integration, performance analysis, or open-source development
• Technical contributions such as code, patches, reviews, research, publications, or substantial academic projects
  

Annual Salary Range for jobs which could be performed in Canada

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