Minimum Qualifications:
- Bachelor's degree or above in Computer Science, Electrical Engineering, or a related field.
- Strong proficiency in C/C++ and Python; solid foundations in algorithms, data structures, and systems programming.
- Hands-on experience in LLM/VLM inference optimization with demonstrated impact on latency, throughput, or serving cost.
- Hands-on experience writing and optimizing GPU kernels in CUDA and/or Triton.
- Deep understanding of GPU architecture (memory hierarchy, occupancy, instruction throughput) with solid optimization experience.
Preferred Qualifications:
- Experience with ML compiler internals (e.g., Triton, MLIR, LLVM).
- Contributions to related open-source projects (e.g., Triton, vLLM, SGLang, FlashAttention, CUTLASS).
- Publications in relevant venues (e.g., MLSys, OSDI, ASPLOS).