AI Infrastructure Engineer

Intel Corporation

. Own the end-to-end optimization pipeline for running state-of-the-art LLMs on Intel GPUs .

Posted 10/9/2026full-timeUnited StatesMid-LevelSenior💰 $170,500 - $315,490 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in optimizing performance for state-of-the-art LLMs on Intel GPUs, with a strong foundation in GPU computing and high-performance computing. Proficient in modern C++ and Python, with hands-on experience in writing and optimizing custom GPU kernels and contributing to open-source inference engines.

Highest-signal resume keywords

GPU ComputingHigh-Performance ComputingModern C++PythonCustom GPU Kernels

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Performance OptimizationAttention MechanismsQuantizationOperator FusionSystematic ProfilingRoofline AnalysisScale-Out Inference OrchestrationComplex Systems-Level Code ModificationAI SystemsInference Paradigms

Tools & Technologies

TritonSYCLCUDACUTLASSVLLMSGLangPyTorchLlama.cpp

Industry Keywords

Artificial IntelligenceMachine LearningOpen-Source ContributionsGenAI Workload DataCPU/GPU Architecture

Tech Stack

Tools & technologies

Node.jsPythonPyTorchC++

About the role

Key responsibilities & impact

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 attention mechanisms, MoE, quantization, and operator fusions
Upstream architectural improvements and hardware backends into open-source repositories such as vLLM, SGLang, and PyTorch
Serve as a bridge between hardware teams and the open-source community
Apply roofline analysis and systematic profiling to decompose bottlenecks
Partner with architecture and compiler teams to shape future GPU roadmaps using real-world GenAI workload data
Advance Intel's AI technology through AI infrastructure and performance optimization

Requirements

What you’ll need

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 of experience; 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
Comfortable reading and modifying complex systems-level code
Understanding of CPU/GPU architecture
Understanding of modern LLM architectures and inference paradigms, including attention mechanisms, KV caching, continuous batching, speculative decoding, and prefill-decode disaggregation
Prior open-source contributions to inference engines such as vLLM, SGLang, PyTorch, or 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
Experience leveraging AI coding agents for workflow and benchmark generation

Benefits

Comp & perks

Competitive pay
Stock bonuses
Health benefits
Retirement benefits
Vacation benefits
Hybrid work model allowing employees to split time between working on-site at their assigned Intel site and off-site