13 days ago
China (city not specified)Senior
Responsibilities
- Optimize TensorFlow, PyTorch, vLLM, and SGLang for AMD GPUs and contribute improvements upstream.
- Develop and tune GPU kernels and performance-critical operators to improve throughput and reduce latency.
- Optimize LLM architectures such as Llama, Qwen, and DeepSeek using FlashAttention, PagedAttention, speculative decoding, and quantization.
- Profile and resolve compute, memory, and communication bottlenecks across multi-GPU and multi-node systems.
- Use compiler technologies and graph compilers to accelerate deep learning and inference pipelines.
- Prototype and integrate optimization methods such as weight-only quantization into production systems.
- Collaborate with internal GPU library teams and open-source maintainers on upstream integration.
- Deliver maintainable, reliable, production-quality performance-critical software.
Requirements
- Deep practical experience with vLLM or SGLang and modern LLMs such as DeepSeek and Qwen.
- Strong knowledge of Transformer, Attention, MoE, and KV Cache concepts and hands-on experience with FlashAttention, PagedAttention, continuous batching, and INT8, INT4, GPTQ, or AWQ quantization.
- Experience profiling and optimizing compute, memory, and communication bottlenecks across multi-GPU and multi-node environments.
- Experience running and optimizing large-scale workloads on heterogeneous clusters.
- Ability to integrate optimized GPU kernels into TensorFlow or PyTorch for large-scale training and inference.
- Strong Python and C++ coding skills, debugging and testing experience, and a record of delivering maintainable performance-critical software.
- Open-source contribution experience and the ability to work independently and collaboratively.
- Bachelor’s and/or Master’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field.
- Experience with CUDA C++ for high-performance GPU kernels or practical experience using Triton to develop and optimize deep learning operators.
- GPU kernel experience with HIP, CUDA, ASM, CK, CUTLASS, or Triton and knowledge of GCN or RDNA architectures is a plus.
- Knowledge of LLVM, ROCm, TVM, or MLIR and distributed inference using Tensor Parallel or Pipeline Parallel is advantageous.
Benefits
- AMD benefits are offered, with details provided through the AMD benefits-at-a-glance materials.
Categories
About AMD
AMD designs and sells CPUs, GPUs, and adaptive/embedded computing products for PCs, data centers, gaming, and edge devices. Its portfolio includes Ryzen and EPYC processors, Radeon and Instinct graphics, and adaptive SoCs from its Xilinx acquisition, sold to OEMs, cloud providers, and device makers. Founded in 1969 and headquartered in Santa Clara, it is a public company on NASDAQ and supplies semi-custom chips for major game consoles.