The AI Infrastructure Engineer role focuses on optimizing LLM inference on Intel's next-generation GPU architectures. The engineer will work across the entire stack to profile performance bottlenecks, develop custom GPU kernels, and integrate optimizations into open-source frameworks. This position requires a deep understanding of AI infrastructure and performance optimization. The engineer will also collaborate with architecture and compiler teams to influence future GPU roadmaps based on real-world data.

• Bachelor's Degree in Computer Science, Software Engineering, Artificial Intelligence/Machine Learning, or related field and 4+ years experience, Masters Degree and 3+ years, OR PhD.
• 3+ years of relevant software engineering experience in GPU computing, AI systems, or high-performance computing (HPC).
• Proficiency in modern C++ and Python.

• Own the end-to-end optimization pipeline for running state-of-the-art LLMs on Intel GPUs.
• Profile, diagnose, and resolve cross-stack performance bottlenecks.
• Design, write, and optimize custom high-performance kernels for critical attention mechanisms, MoE, quantization, and operator fusions.
• Upstream architectural improvements and hardware backends into open-source repositories like vLLM, SGLang, and PyTorch.
• Apply roofline analysis and systematic profiling to decompose bottlenecks and shape future GPU roadmaps.

• Intel offers a comprehensive benefits package including competitive pay, stock programs, healthcare coverage, retirement plans, paid time off, parental leave, and programs supporting employee wellbeing and professional development.