AMDAI Framework Eng.

任职要求

Skilled engineer with strong technical and analytical expertise in C++ development within Linux environments. The ideal candidate will thrive in both collaborative team settings and independent work, with the ability to define goals, manage development efforts, and deliver high-quality solutions. Strong problem-solving skills, a proactive approach, and a keen understanding of software engineering best practices are essential. 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. MANDATORY EXPERIENCE:  Inference Frameworks, Model Architectures & Optimization Expertise: Deep practical experience with vLLM or SGLang, mastery of modern LLMs (e.g., DeepSeek, Qwen), strong theoretical grounding in Transformer/Attention/MoE/KV Cache, an…

工作职责

THE ROLE:  As a core member of the team, you will play a pivotal role in optimizing and developing deep learning frameworks for AMD GPUs. Your experience will be critical in enhancing GPU kernels, deep learning models, and training/inference performance across multi-GPU and multi-node systems. You will engage with both internal GPU library teams and open-source maintainers to ensure seamless integration of optimizations, utilizing cutting-edge compiler technologies and advanced engineering principles to drive continuous improvement.