Intern - SMS Pathfinding Engineering

What you'll do

• Develop and validate photonic link-budget and system-level models for optical memory fabrics.
• Compare performance, latency, bandwidth, power, reliability, and scalability across photonic architectures and memory technologies; assess CXL-based memory expansion for AI workloads.
• Collaborate on research and communicate findings through technical reports, white papers, presentations, and architecture recommendations.

What they ask for

• Currently pursuing a Master's or PhD in Electrical Engineering, Computer Engineering, Photonics, Optics, Applied Physics, or a related technical discipline.
• 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.
• Strong analytical, communication, presentation, and collaborative problem-solving skills.

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, photonic simulation tools, or AI infrastructure.

Tools

Python, CXL, PCIe, Lumerical, GDSFactory, MEEP, SAX, Simphony

AI focus

Researches photonic interconnects and disaggregated memory architectures for future AI systems; uses AI-enabled tools to improve productivity, analysis, problem-solving, documentation, and decision-making.