AI Framework Engineer

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.

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:

• End to end optimization:  Build and optimize end to end distributed inference (e.g, P/D disaggregation and Large-EP) and RL solutions on mainstream frameworks like vLLM and SGlang.
• Collaborate with GPU Library Teams:  Work closely with internal teams to analyze and improve training and inference performance on AMD GPUs.
• Collaborate with Open-Source Maintainers:  Engage with framework maintainers to ensure code changes are aligned with requirements and integrated upstream.
• Work in Distributed Computing Environments:  Optimize deep learning performance on both scale-up (multi-GPU) and scale-out (multi-node) systems.
• Utilize Cutting-Edge Compiler Tech:  Leverage advanced compiler technologies to improve deep learning performance.
• Optimize Deep Learning Pipeline:  Enhance the full pipeline, including integrating graph compilers.
• Software Engineering Best Practices:  Apply sound engineering principles to ensure robust, maintainable solutions.

PREFERRED EXPERIENCE:

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