Compiler Engineering Intern

Intel is hiring a Compiler Engineering Intern in Shanghai, China. Level rates it ; you can apply on Level.

AI in this role

deepseekhugging-facevllmpytorch

Job Details:

Job Description:

Join Us in Shaping the Future
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 Team
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.
Position Overview
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 an engineering or research project that may involve compiler features, MLIR-based intermediate representations, Triton or other Domain Specific Languages (DSLs), optimization, code generation, runtime or offload support, debug information, testing infrastructure, performance analysis, or developer tools. Projects may connect hardware capabilities to AI frameworks such as PyTorch, inference engines such as vLLM and SGLang, and machine-learning model ecosystems such as DeepSeek, Hugging Face, and Qimi. You will investigate technical problems, implement and test solutions, analyze results, and communicate findings to the team. This opportunity is designed for a graduate student who can apply deeper technical expertise to an ambiguous engineering or research problem, evaluate alternatives, and deliver a well-supported solution or prototype.
Responsibilities
• 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.

Qualifications:

Minimum Qualifications
• Currently pursuing a Bachelor or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, Applied Mathematics, or a related technical discipline.
• Graduate-level coursework, research, or project experience in compilers, programming languages, computer architecture, parallel computing, systems software, or a closely related area.
• Experience programming in C or C++ and working with complex software systems.
• Experience designing experiments, analyzing technical results, and communicating conclusions.
Preferred Qualifications
• Hands-on experience with LLVM or related compiler infrastructure, including front-end development, intermediate representations, optimization passes, code generation, runtimes, or debug information.
• Research or development experience involving CPU/GPU architectures, heterogeneous computing, AI/ML compilation, HPC, parallel programming, or performance optimization.
• Familiarity with LLVM, MLIR, Clang, Flang, SYCL/DPC++, Triton, OpenMP, CUDA, or another compiler or parallel-programming ecosystem.
• Experience with AI frameworks such as PyTorch, inference engines such as vLLM and SGLang, and machine-learning model ecosystems such as DeepSeek, Hugging Face, and Qimi.
• Tile-base programming model knowledge
• GPU Compute kernel writing experience
• Experience contributing to open-source software or working in a large collaborative codebase.
• Publications, technical reports, patents, open-source contributions, or other evidence of advanced technical work.
Success in This Role
• Curiosity and willingness to learn unfamiliar technologies.
• Strong analytical, problem-solving, and debugging skills.
• Clear written and verbal communication.
• Ability to work independently on scoped tasks while seeking guidance when needed.
• Collaborative mindset and commitment to high-quality engineering.

Job Type:

Student / Intern

Shift:

Shift 1 (China)

Primary Location:

PRC, Shanghai

Additional Locations:

Posting Statement:

All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance.

Position of Trust

N/A

Work Model for this Role

This role will be eligible for our hybrid work model which allows employees to split their time between working on-site at their assigned Intel site and off-site. * Job posting details (such as work model, location or time type) are subject to change.

ADDITIONAL INFORMATION: Intel is committed to Responsible Business Alliance (RBA) compliance and ethical hiring practices. We do not charge any fees during our hiring process. Candidates should never be required to pay recruitment fees, medical examination fees, or any other charges as a condition of employment. If you are asked to pay any fees during our hiring process, please report this immediately to your recruiter.

How we rate this

Compiler Engineering Intern at Intel rates 91 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Key facts

Compiler Engineering Intern is an open job at Intel.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in PRC, Shanghai.
The job was posted on October 8, 2026.
Seniority: intern.
Applications are handled by Intel. Apply on Intel

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

DeepseekHugging FacevLLMPyTorch

Questions you could be asked

What's a project where you used Deepseek hands-on?
Walk me through how you've used Hugging Face in your day-to-day work.
What are the limits of vLLM that you've run into, and how did you work around them?
What's a project where you used PyTorch hands-on?
How would you decide a model or AI system is ready to ship?

Adapt your resume

List these exact terms on your resume: Deepseek, Hugging Face, vLLM, and PyTorch. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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