Sr. Software System Designer

AMD AMD · Semiconductors · Shanghai, China · Engineering

This role focuses on the development, debugging, optimization, and technical support of machine learning end-to-end custom software solutions for AMD server GPUs. The engineer will work on kernel operators, frameworks, distributions, compilers, and performance optimizations for both inference and training, with a strong emphasis on GPU kernel primitives and AI frameworks.

What you'd actually do

  1. Position technical proposals and support to top customers.
  2. Provide significant contribution to customer PoC success.
  3. Drive custom requirements for AI SW performance and stability, including from POC requirement to POR release, from GPU kernel to frameworks and distribution solutions.
  4. Collaborate and interact with different teams to analyze and optimize training and inference workloads from kernels, frameworks to solutions
  5. Analyze competitive solutions to identify strength and weakness for articulate value propositions.

Skills

Required

  • machine learning kernel operators (like MHA, MLA, MOE etc.)
  • Triton/DSL, cuda/hip, PTX/ASM etc.
  • cutlass/CK etc.
  • frameworks, distributions, compilers
  • performance optimizations for inference or training
  • C++
  • Python
  • industry AI use scenarios and solutions
  • end-to-end pipelines
  • frameworks or SDKs
  • parallel programming
  • debugging
  • development skillsets

Nice to have

  • Linux ROCm/CUDA runtime and KMD/UMD driver
  • AI distribution solutions (i.e. EP/SP/CP/TP/PP/DP, DeepEp, DualPipe, PD aggregation etc., KV cache transfer and storage)
  • AI distributed network communication with multi-GPU and multi-node collective communication primitives (NCCL/RCCL), NIC/GPU drivers for RDMA/GDR and high-speed network etc.
  • Linux OS/driver, CI and toolchain (profiler/DCGM) development and debugging
  • compiler (Torch, Triton, LLVM, XLA HLO, graph)
  • model inference optimization process like GEMM/convolution tuning, graph optimization and operator fusion
  • AI frameworks(e.g. vLLM, Sglang, Megatron-LM, Deepspeed, TensorRT, TensorRT-LLM etc.)

What the JD emphasized

  • machine learning E2E custom software solutions
  • machine learning areas
  • kernel operators
  • performance optimizations for inference or training
  • industry AI use scenarios and solutions
  • end-to-end pipelines
  • AI SW performance and stability
  • training and inference workloads
  • model inference optimization process

Other signals

  • optimization
  • performance
  • inference
  • training
  • GPU kernel