Evaluate protocol and system-level impacts of CXL-based memory expansion and disaggregated memory architectures for AI workloads.
Utilize AI-enabled tools and technologies to improve productivity, data analysis, technical problem solving, documentation, and decision-making.
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.
Minimum Qualifications
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.
Currently pursuing a Master's or PhD degree 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.
Preferred Qualifications
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.
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