AI Infrastructure Engineer

About the role

We are looking for a performance-obsessed AI Infrastructure Engineer to push LLM inference to its absolute limits on Intel's next-generation GPU architectures. In this role, you will dive deep into the inference stack and redefine peak performance. Your optimizations will be instrumental in unlocking the full potential of Intel hardware for state-of-the-art generative AI workloads.

Responsibilities

• Drive Inference Performance: Own the end-to-end optimization pipeline for running state-of-the-art LLMs on Intel GPUs.
• Deep Stack Optimization: Profile, diagnose, and resolve cross-stack performance bottlenecks.
• Kernel Development and Integration: Design, write, and optimize custom high-performance kernels for critical attention mechanisms, MoE, quantization, and operator fusions.
• Open Source Leadership: Upstream your architectural improvements and hardware backends directly into open-source repositories like vLLM, SGLang, and PyTorch, acting as a bridge between the hardware teams and the open-source community.
• Shape the Hardware Roadmap: Apply roofline analysis and systematic profiling to decompose bottlenecks. Partner with architecture and compiler teams to shape future GPU roadmaps based on real-world GenAI workload data.

Requirements

• Bachelor’s Degree in Computer Science, Software Engineering, Artificial Intelligence/Machine Learning, or related field and 4+ years of experience, Master’s 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. You are comfortable reading and modifying complex systems-level code.

Preferred Qualifications

• Understanding of CPU/GPU architecture.
• Understanding of modern LLM architectures and inference paradigms: attention mechanisms, KV caching, continuous batching, speculative decoding, and prefill-decode disaggregation.
• Prior open-source contributions to inference engines (vLLM, SGLang, PyTorch, llama.cpp).
• Hands-on experience writing and optimizing custom GPU kernels using Triton, SYCL, CUDA/CUTLASS, or other DSLs.
• Experience with scale-out inference orchestration across multi-node topologies.
• Leverage AI coding agents daily to accelerate your own workflow and benchmark generation.

Benefits

We offer a total compensation package that ranks among the best in the industry. It consists of competitive pay, stock bonuses, and benefit programs which include health, retirement, and vacation.

Pay

Annual Salary Range for jobs which could be performed in the US: $170,500.00 - $315,490.00 USD. The range displayed reflects the minimum and maximum target compensation for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.

Schedule

This role will be eligible for our hybrid work model which allows employees to split their time between working on-site at their assigned Intel site and off-site.

Locations: US, California, Santa Clara; US, California, Folsom; US, Oregon, Hillsboro; US, Texas, Austin.