23 hours ago
Zürich, SwitzerlandIntern
Responsibilities
- Design and implement Python DSL features for GPU kernel authoring, including metaprogramming and IR generation.
- Develop MLIR dialects, transformation passes, and lowering pipelines for GPU compiler optimization.
- Design and optimize GPU kernels and evaluate the effect of compiler transformations on generated code and performance.
- Profile, benchmark, and validate compiler and kernel improvements across representative workloads.
Requirements
- 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 Python DSLs, metaprogramming, staged execution, and translation into compiler IR.
- Experience with MLIR, including IR construction, transformation passes, or lowering.
- Practical GPU programming experience, preferably with 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.
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.