Deep Learning Compiler Engineer - Cuda

NVIDIA NVIDIA · Semiconductors · Shanghai, China +1

NVIDIA is seeking a Deep Learning Compiler Engineer to design and implement the DSL and core compiler for emerging GPU architectures, focusing on optimizing performance for AI/LLM workloads. This role involves investigating next-generation GPU architectures and integrating with AI/ML frameworks.

What you'd actually do

  1. Design and implement the DSL and the core compiler of tile-aware GPU programming model for emerging GPU architectures
  2. Continuously innovate and iterate on the core architecture of the compiler to consistently optimize performance
  3. Investigation of next-generation GPU architectures and provide solutions in the DSL and compiler stack
  4. Performance analysis on emerging AI/LLM workloads and integrate with AI/ML frameworks

Skills

Required

  • Masters or PhD or equivalent experience in relevant discipline (CE, CS&E, CS, AI)
  • 2+ years of relevant work experience
  • Excellent C/C++ programming and software engineering skills
  • Good fundamental knowledges on computer architecture
  • Strong ability in abstracting problems and the methodology in resolving problems

Nice to have

  • ACM background is a plus
  • Strong compiler backgrounds including MLIR/TVM/Triton/LLVM is desired
  • Good knowledge of GPU architecture and fast kernel programming skills is a plus
  • Knowledge of LLM algorithms or a certain HPC domain is a plus
  • Knowledge of multi-GPU distributed communication is a plus
  • Excellent oral communication in English is a plus

What the JD emphasized

  • Masters or PhD or equivalent experience in relevant discipline (CE, CS&E, CS, AI)
  • 2+ years of relevant work experience
  • Excellent C/C++ programming and software engineering skills
  • Good fundamental knowledges on computer architecture
  • Strong compiler backgrounds including MLIR/TVM/Triton/LLVM is desired

Other signals

  • design and implement DSL and core compiler for emerging GPU architectures
  • optimize performance of compiler
  • performance analysis on AI/LLM workloads