Intern - SMS Pathfinding Engineering - Richardson, TX, United States

Originally posted 21 October 2026 by the employer.

About the role

As an intern in the Storage and Memory Systems (SMS) Pathfinding group, you will research photonic interconnect technologies that enable disaggregated memory architectures for AI systems. You will develop modeling and analysis tools to evaluate silicon photonic memory fabrics and assess system-level tradeoffs across optical interconnects.

What you'll do

- Develop parameterized photonic link budget and system-level models to evaluate optical memory fabrics, including lasers, modulators, waveguides, switches, photodetectors, and interconnect architectures. - Analyze performance, latency, bandwidth, power efficiency, reliability, and scalability tradeoffs across photonic memory architectures and memory technologies such as HBM, DDR5, and LPDDR. - Evaluate protocol and system-level impacts of CXL-based memory expansion and disaggregated memory architectures for AI workloads. - Calibrate and validate models using industry data, proof-of-concept platforms, simulations, and available measurement results. - Collaborate with architects, researchers, and industry partners to communicate findings through technical reports, white papers, presentations, and architecture recommendations.

What you'll need

- Currently pursuing a Master's or PhD degree in Electrical Engineering, Computer Engineering, Photonics, Optics, Applied Physics, or a related technical discipline. - 0-2 years of relevant experience through research, internships, academic projects, publications, or coursework in photonics, optical interconnects, computer architecture, memory systems, or related fields. - Coursework or research experience in silicon photonics, integrated optical devices, computer architecture, memory systems, interconnects, or system design. - Proficiency in Python and scientific computing tools for modeling, simulation, and data analysis.

Nice to have

- Experience with photonic link budget analysis, optical transceivers, optical communications, or photonic system modeling. - Familiarity with CXL, PCIe, memory fabrics, memory expansion architectures, or large-scale system interconnects. - Experience with photonic simulation tools such as Lumerical, GDSFactory, MEEP, SAX, Simphony, or similar platforms. - Knowledge of AI infrastructure, AI inference workloads, GPU memory systems, memory pooling, or large language model architectures. - Publications, research contributions, or hands-on experience involving photonic systems, optical networks, memory architectures, hardware characterization, or experimental validation.

Skills: Photonic link budget, Silicon photonics, Optical interconnects, CXL, HBM