AI Hardware Engineering Intern

Intel Corporation

. Conduct technical research on next-generation AI infrastructure, including CPUs, GPUs, AI accelerators, ASICs, memory systems, networking, storage, rack-scale architecture, power delivery, cooling, and cluster management .

Posted 10/23/2026internshipSanta Clara • California • United StatesEntry Level💰 $100,598 - $100,602 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in AI infrastructure, including CPUs, GPUs, and AI accelerators, with a strong foundation in computer architecture and systems software. Proficient in evaluating AI workloads and developing technical models for hardware/software trade-offs.

Highest-signal resume keywords

PhD In Computer ScienceComputer ArchitectureProgramming In C/C++/Rust/CUDAAI Hardware PlatformsInference Frameworks

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Computer ArchitectureOperating SystemsDistributed SystemsParallel ComputingAI/ML SystemsHigh-Performance ComputingProfiling ToolsBenchmarking ToolsSimulation And ModelingLow-Level Debugging

Tools & Technologies

LinuxKubernetesContainersIntel Xeon ProcessorsNVIDIA GPUsAMD GPUsTPUsHugging Face TransformersPyTorchTriton

Industry Keywords

AI InfrastructureGenerative AI ModelsInference ConceptsMemory HierarchiesVLSIASIC/FPGA SystemsKernel DevelopmentData MovementCluster ManagementOpen-Source Contributions

Tech Stack

Tools & technologies

CloudDistributed SystemsKubernetesLinuxPyTorchRayRustC++

About the role

Key responsibilities & impact

Conduct technical research on next-generation AI infrastructure, including CPUs, GPUs, AI accelerators, ASICs, memory systems, networking, storage, rack-scale architecture, power delivery, cooling, and cluster management
Analyze GenAI and agentic AI workloads and their hardware implications
Evaluate AI inference pipelines, including prefill and decode, memory capacity and bandwidth, KV-cache management, compute utilization, interconnect bandwidth, and latency
Identify product and system gaps affecting Intel’s ability to serve AI customers
Support silicon hardware programs through multiple phases of the silicon lifecycle, from concept to production silicon
Develop technical recommendations for products, optimization, enablement, partnerships, and AI customer requirements
Benchmark and compare AI hardware and infrastructure approaches across performance, power efficiency, cost, programmability, memory hierarchy, scalability, ecosystem maturity, and workload fit
Develop technical models, simulations, prototypes, and analysis tools for AI workload hardware/software trade-offs
Investigate system bottlenecks across compute, memory, networking, storage, and software orchestration
Work with Intel technical teams and external ISVs to collect data on model requirements, inference bottlenecks, deployment challenges, hardware opportunities, and customer priorities
Engage with AI software ecosystem partners, including model providers, inference-engine developers, cloud providers, framework teams, and ISVs
Present findings through architecture diagrams, benchmark analyses, technical reports, design recommendations, and executive-ready presentations

Requirements

What you’ll need

Currently pursuing a PhD in Computer Science, Computer Engineering, Electrical Engineering, AI/ML, Systems Engineering, or a related technical discipline
Strong foundation in computer architecture, operating systems, distributed systems, parallel computing, AI/ML systems, high-performance computing, or systems software
Familiarity with generative AI models and inference concepts, including transformers, attention, token generation, model serving, quantization, batching, KV cache, long-context workloads, and latency-throughput trade-offs
Experience programming in C, C++, Rust, CUDA, SYCL, OpenCL, or a comparable low-level or performance-oriented language
Strong academic performance with a cumulative GPA of 3.0 or higher required
Coursework, research, or project experience in computer architecture, VLSI, digital design, ASIC/FPGA systems, compilers, kernel development, GPU programming, distributed systems, high-performance computing, or AI systems
Experience using profiling, tracing, or benchmarking tools
Familiarity with AI hardware platforms such as Intel Xeon processors, Intel GPUs, NVIDIA GPUs, AMD GPUs, TPUs, custom AI accelerators, FPGAs, or domain-specific inference ASICs
Understanding of memory hierarchies and performance concepts, including cache behavior, DRAM, HBM, memory bandwidth, memory capacity, latency, data locality, DMA, and data movement
Experience with inference frameworks or runtimes such as vLLM, SGLang, Hugging Face Transformers, PyTorch, Triton, or Ray
Familiarity with AI systems infrastructure such as Linux, containers, Kubernetes, distributed storage, RDMA over Ethernet, InfiniBand, CXL, PCIe, NVMe, UALink/eSUN based interconnects, silicon photonics, and cluster-management software
Some experience with simulation, modeling, emulation, RTL, FPGA prototyping, firmware, driver development, or low-level debugging
Some experience contributing to open-source systems, ML infrastructure, compiler, kernel, framework, or performance-engineering projects

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 and off-site