Tech mentioned
- Python
- C++
- CUDA
- Triton
- Location
- Zurich, Switzerland
- Workplace
- On-site
- Salary
- Not listed
- Seniority
- Intern
- Employment
- Full-time
- Team
- Not specified
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.