Software Development Engineer

这是一个专注于为AMD GPU优化深度学习框架和大型语言模型（LLM）的软件开发工程师职位

KEY RESPONSIBILITIES: Deep Learning & LLM Framework Optimization: Optimize major DL/LLM frameworks (TensorFlow, PyTorch , vLLM , SGLang ) for AMD GPUs and contribute improvements upstream. GPU Kernel & Operator Optimization: Develop and tune GPU kernels and performance-critical operators to maximize throughput and minimize latency. Model & Architecture Optimization: Adapt and optimize LLM architectures (e.g., Llama, Qwen, DeepSeek) and apply advanced techniques like FlashAttention , PagedAttention , and quantization. End-to-End Performance Engineering: Perform comprehensive profiling to identify bottlenecks and implement system, memory, and communication optimizations across multi-GPU and multi-node setups. Compiler & Pipeline Acceleration: Leverage advanced compiler technologies and graph compilers to enhance the full deep learning and inference pipeline. Research & Advanced Techniques: Prototype and integrate emerging optimization methods such as speculative decoding and weight-only quantization into production systems. Cross-Team & Open-Source Collaboration: Collaborate with internal GPU library teams and open-source maintainers to align improvements and ensure seamless upstream integration. Software Engineering Excellence: Apply robust engineering practices to deliver maintainable, reliable, and production-quality performance optimizations.

GPU Kernel Development & Optimization is a plus : Hands-on experience designing and tuning high-performance GPU kernels for AMD GPUs using HIP, CUDA, ASM, and tools like CK, CUTLASS, and Triton, with strong knowledge of GCN/RDNA architectures. Compiler & System-Level Optimization is a plus : Solid foundational knowledge of LLVM, ROCm , and compiler-driven techniques for improving kernel and system performance. ACAD EMIC & PREFERRED QUALIFICATIONS : Master’s Degree or PHD in Computer Science , Computer Engineering, Electrical Engineering, or a related field . Low-Level Development Skills: Experience with CUDA C++ programming for writing and debugging high-performance GPU kernels; or practical experience using Triton to develop and optimize deep learning operators. Compiler Knowledge: Understanding or practical experience with compiler technologies like TVM or MLIR is a significant advantage. Distributed Systems Experience: Hands-on experience with distributed inference for large-scale models (e.g., Tensor Parallel, Pipeline Parallel).