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# AI Infrastructure Engineer

- **Company:** Intel Corporation
- **Location:** Hillsboro, OR, United States
- **Experience:** Experienced
- **Salary:** $170,500.0 - $315,490.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Nvidia CUDA, General-Purpose Computing on Graphics Processing Units, Python (Programming Language), Linux Kernel, Machine Learning, Open Source Technology, Performance Tuning, Software Engineering, AI Infrastructure, Graphics Processing Unit (GPU), High Performance Computing, Pytorch, Large Language Models, AI Coding Agents, Attention Mechanisms, Information Technology, SGLang, VLLM, Model Inference, C++14, CUTLASS, llama.cpp
- **Published:** October 16, 2026
- **Apply:** https://www.juju.com/job/1_ScrapingHub22000-JR0286233

## About the Role

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.

## 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.