AI Hardware Engineering Intern

• Conduct technical research on the next generation of 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 to understand their hardware implications, including transformer architecture based inference, mixture-of-experts architectures, multimodal models, real-time interactive inference, and distributed serving.
• Evaluate the hardware behavior of AI inference pipelines, including the distinct demands of prompt processing/prefill and token generation/decode, memory capacity, memory bandwidth, KV-cache management, compute utilization, interconnect bandwidth, and latency. For example, cluster architecture around optimizing both prefill and decode through hardware, memory, and system co-design.
• Identify product and system gaps that may limit Intel's ability to serve AI customers effectively, including hardware capabilities, developer tooling, performance libraries, reference architectures, deployment models, observability, and ecosystem enablement.
• Expertise in modern high-performance CPU, GPU, or AI accelerator architecture and hardware/software interactions.
• Support Silicon HW programs through multiple phases of the silicon lifecycle, with end-to-end chip execution from concept to silicon.
• Good understanding with firmware codebases, compilers, kernels, serving stacks and model architectures.
• Familiar with driving silicon design for subsystems and/or top-level functions with ASICs and SOCs from an architecture specification to production silicon including memory subsystems and interconnects: scale up, scale out, scale across, power management , liquid cooling at rack scale level, deep system level observability & telemetry and dependent impact into Gen AI and LLM level performance/tco/execution KPIs
• Develop technical recommendations for what Intel should build, optimize, enable, partner on to meet AI customer requirements based on deep understanding of customer use cases, workload performance, and ML systems research.
• Benchmark and compare relevant AI hardware and infrastructure approaches across performance, power efficiency, cost, programmability, memory hierarchy, scalability, ecosystem maturity, and workload fit.
• Develop technical models, simulations, prototypes, or analysis tools to evaluate hardware/software trade offs for AI workloads.
• Investigate system bottlenecks across compute, memory, networking, storage, and software orchestration, and propose methods to improve end-to-end AI workload performance.
• Work with Intel technical teams and external ISVs to collect data on model requirements, inference bottlenecks, deployment challenges, hardware optimization opportunities, and customer priorities.
• Engage with AI software ecosystem partners, including model providers, inference-engine developers, cloud providers, framework teams, and ISVs, to understand how their stacks interact with underlying hardware.
• Good understanding of modern AI software layers, including PyTorch, Hugging Face Transformers, vLLM, SGLang, model serving, TensorRT-LLM, Triton, Kubernetes, and Neocloud design efficiencies.
• Present findings through architecture diagrams, benchmark analyses, technical reports, design recommendations, and executive-ready presentations.

Qualifications:

• 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 one or more of the languages - 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 to evaluate CPU, GPU, accelerator, network, memory, storage, or software performance.
• 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.

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.