AI Infrastructure Engineer

Intel; Hillsboro (OR); Hybrid; USD 189,000 - 315,000; Full time

Benefits offered by this job

Stock bonuses

Health benefits

Retirement plan

Paid vacation

Job summary

Intel is seeking a performance-driven AI Infrastructure Engineer to push LLM inference on next-gen Intel GPUs. You will profile, optimize, and write high-performance kernels, upstream improvements into vLLM and PyTorch, and collaborate with architecture teams to shape future GPU roadmaps.

You will contribute across the stack from kernel design to open-source integration, targeting state-of-the-art generative AI workloads and multi-node scale-out setups with a strong emphasis on performance and

Qualifications

• Bachelor's degree in Computer Science, Software Engineering, AI/ML or related field with 4+ years experience, Masters with 3+ years, OR PhD.
• 3+ years of relevant software engineering experience in GPU computing, AI systems, or HPC.
• Proficiency in modern C++ and Python; comfortable reading and modifying complex systems-level code.

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 attention, 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.

Skills

C++

Python

GPU computing

HPC

CPU/GPU architecture

LLM architectures

Open-source contributions

Kernel programming

Multi-node orchestration

AI coding agents

Education

Bachelor's degree in CS/related field

Master's degree in CS/related field

PhD

Tools

Triton

SYCL

CUDA/CUTLASS

PyTorch

vLLM

SGLang

Job description

Job Details

Job Description:

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.

Qualifications

Minimum Qualifications

• 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.

Job Type

Experienced Hire

Shift

Shift 1 (United States of America)

Primary Location

US, California, Santa Clara

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.

Position of Trust

N/A

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. 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.

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.