AI Infrastructure Engineer

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. You will work end-to-end across the stack: profiling bottlenecks, writing custom GPU kernels, and upstreaming your optimizations directly into industry-standard serving frameworks like vLLM and SGLang.

Your optimizations will be instrumental in unlocking the full potential of Intel hardware for state-of-the-art generative AI workloads.

What You Will Do - 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.

You will partner with our architecture and compiler teams to shape future GPU roadmaps based on real-world GenAI workload data.

- Show passion about AI infrastructure and performance optimization.

- Bachelors 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.

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. - You leverage AI coding agents daily to accelerate your own workflow and benchmark generation.

Your expertise will play a vital role in advancing Intel's AI technology. We invite you to bring your skills, experience, and passion for AI to make an impact-apply today.

Additional Locations: US, California, Folsom, US, Oregon, Hillsboro, US, Texas, Austin

Posting Statement: All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance.

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. Find out more about the benefits of working at Intel .

Annual Salary Range for jobs which could be performed in the US: $170,500.00-315,490.00 USD

The range displayed on this job posting 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. Your recruiter can share more about the specific compensation range for your preferred location during the hiring process.

Work Model for this Role 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. * Job posting details (such as work model, location or time type) are subject to change.

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ADDITIONAL INFORMATION: Intel is committed to Responsible Business Alliance (RBA) compliance and ethical hiring practices. We do not charge any fees during our hiring process. Candidates should never be required to pay recruitment fees, medical examination fees, or any other charges as a condition of employment.

If you are asked to pay any fees during our hiring process, please report this immediately to your recruiter.