AI Hardware Engineering Intern

Job Title

AI Hardware Engineering Intern

Role Summary

Internship on an AI hardware and infrastructure engineering team focused on hardware/software co-design for AI inference and datacenter deployment. Work covers analysis of model behavior, memory and interconnect demands, performance, power, and system-level trade-offs.

Location is hybrid with primary sites in Santa Clara, CA and Folsom, CA. The role involves hands-on research, benchmarking, modeling, and collaboration with hardware and software teams.

Experience Level

Internship - Entry-level (student intern). Role targets graduate research interns (PhD students specifically) with research or project experience in systems, architecture, or AI.

Responsibilities

Conduct technical research and analysis to evaluate hardware/software trade-offs for AI inference and infrastructure.

• Research CPU/GPU/accelerator architecture, memory systems, networking, storage, rack-scale architecture, and power/cooling implications for AI workloads.
• Analyze GenAI and agentic AI workloads (transformers, MoE, multimodal, real-time inference) to identify hardware implications.
• Evaluate inference pipeline behavior (prefill vs decode, KV-cache, memory bandwidth, utilization, latency).
• Benchmark and compare hardware/infrastructure approaches across performance, power, cost, and scalability metrics.
• Develop models, simulations, prototypes, or analysis tools to quantify hardware/software trade-offs.
• Identify system-level bottlenecks and propose architecture, software, or deployment improvements.
• Collaborate with internal technical teams and external ISVs, model and framework providers, and cloud partners to collect data and validate findings.
• Present results via diagrams, technical reports, benchmarks, and executive-ready summaries.

Requirements

Core technical skills and experience required or strongly preferred for successful contribution.

• Must-have: Strong foundation in computer architecture, operating systems, distributed systems, parallel computing, AI/ML systems, or systems software.
• Must-have: Familiarity with generative AI inference concepts (transformers, attention, token generation, batching, quantization, KV cache, latency vs throughput).
• Must-have: Experience programming in at least one performance-oriented language such as C, C++, Rust, CUDA, SYCL, or OpenCL.
• Must-have: Ability to perform independent technical research, benchmark systems, and produce quantitative analysis.
• Nice-to-have: Coursework, research, or projects in VLSI, digital design, ASIC/FPGA, compilers, kernel development, GPU programming, or AI systems.
• Nice-to-have: Experience using profiling, tracing, or benchmarking tools for CPU/GPU/accelerator/network/memory/storage performance.
• Nice-to-have: Familiarity with inference runtimes and frameworks (PyTorch, Hugging Face, vLLM, Triton, TensorRT-LLM) and container/orchestration environments (Linux, containers, Kubernetes).
• Nice-to-have: Experience with simulation, modeling, FPGA prototyping, firmware/driver development, or low-level debugging; contributions to open-source systems or ML infrastructure.

Education Requirements

Currently pursuing a PhD in Computer Science, Computer Engineering, Electrical Engineering, AI/ML, Systems Engineering, or a related technical discipline. Strong academic performance expected (cumulative GPA 3.0 or higher). "Related technical discipline" is acceptable where noted.

About the Company

Company: Intel Corporation

Headquarters: Santa Clara, California, USA

Intel Corporation is a leading multinational technology company known for its innovative semiconductor solutions, including microprocessors, artificial intelligence accelerators, and memory products. Headquartered in the United States, Intel focuses on cutting-edge technology and a collaborative working environment, driving advancements in semiconductor manufacturing to meet global demands. The company emphasizes professional development and aims to shape the future of technology through groundbreaking designs.