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GPU Compiler Intern

CompanyNVIDIA
LocationZurich
Work ModeOn Site
PostedNovember 11, 2026
Internship Information
Core Responsibilities
You will design and implement Python DSL features and MLIR-based compiler infrastructure to translate Python programs into high-performance NVIDIA GPU kernels. Additionally, you will profile, benchmark, and validate compiler optimizations to improve kernel performance across various workloads.
Internship Type
full time
Company Size
52800
Visa Sponsorship
No
Language
English
Working Hours
40 hours
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About The Company
Since its founding in 1993, NVIDIA (NASDAQ: NVDA) has been a pioneer in accelerated computing. The company’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined computer graphics, ignited the era of modern AI and is fueling the creation of the metaverse. NVIDIA is now a full-stack computing company with data-center-scale offerings that are reshaping industry.
About the Role

As GPU Compiler Intern, you will develop compiler infrastructure for Python-based GPU programming, including CUTLASS CuTeDSL. You will work on DSL design, MLIR IR design, and compiler optimizations that translate expressive Python programs into high-performance NVIDIA GPU kernels.


What you will be doing:

  • Design and implement Python DSL features for GPU kernel authoring, including metaprogramming and IR generation.
  • Develop MLIR dialects, transformation passes, and lowering pipelines that preserve the information needed for effective GPU optimization.
  • Design and optimize GPU kernels, exploring how IR design and compiler transformations affect generated code and performance.
  • Profile, benchmark, and validate compiler and kernel improvements across representative workloads.

What we need to see:

  • Currently pursuing or holding a Bachelor’s, Master’s, or Ph.D. degree in Computer Science, Computer Engineering, or a related technical discipline, or equivalent practical experience.
  • Proficiency in Python and C++, with strong debugging and software development skills.
  • Understanding of how Python DSLs work, including metaprogramming, staged execution, and translation into compiler IR.
  • Experience with MLIR, including IR construction, transformation passes, or lowering.
  • Practical experience with GPU programming, preferably CUDA, and knowledge of GPU execution and memory models.
  • Familiarity with performance analysis and optimization, including profiling, memory access patterns, and parallel execution.
  • Experience with CUTLASS, CuTeDSL, Triton, LLVM, or other GPU compiler and kernel development tools is a plus.

Key Skills
PythonC++MLIRGPU ProgrammingCUDACompiler DesignDSL DesignMetaprogrammingPerformance ProfilingParallel ExecutionMemory ModelsCUTLASSCuTeDSLTritonLLVM
Categories
TechnologySoftwareEngineeringData & AnalyticsScience & Research