AI Hardware Engineering Intern

What you'll do

• Research and benchmark AI hardware, infrastructure, and inference workloads; investigate performance bottlenecks and trade-offs.
• Develop models, simulations, prototypes, or analysis tools and recommend hardware, software, and system improvements.
• Collaborate with Intel teams and ecosystem partners, then present findings in technical reports, diagrams, and presentations.

What they ask for

• 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, token generation, model serving, quantization, batching, and KV cache.
• Experience programming in one or more of C, C++, Rust, CUDA, SYCL, OpenCL, or a comparable low-level or performance-oriented language; cumulative GPA of 3.0 or higher.

Nice to have

• Coursework, research, or project experience in computer architecture, VLSI, digital design, ASIC/FPGA systems, compilers, GPU programming, distributed systems, or AI systems.
• Experience with performance profiling or benchmarking tools, AI hardware platforms, inference frameworks, or AI systems infrastructure.

Pay

$100,598.00-100,602.00 USD (target compensation; pay basis not specified)

Tools

C, C++, Rust, CUDA, SYCL, OpenCL, PyTorch, vLLM

AI focus

AI infrastructure and inference hardware/software co-design, including analysis of GenAI and agentic AI workloads, inference pipelines, and end-to-end performance.