Senior Researcher - AI Computer Architecture

Define the research roadmap and advance a long-term agenda for AI system architectures built on novel optical interconnects, new memory technologies and other recent hardware trends. You will identify research gaps across optical IOs, memory systems, and AI workloads, and lead influential projects across these areas. Cooperate closely with other discipline experts to steer the overall research program and ensure mutual directional alignment on the most impactful strategic research priorities. You will work in a multidisciplinary team and coordinate across domains and stakeholders to identify and solve problems. Design, model, and prototype future AI systems through an integrated co-design approach. You will build system models and simulations to explore architectural trade-offs in scale-up networking, memory offloading, and memory disaggregation, characterize agentic AI workload demands, and develop software prototypes to validate concepts end-to-end. You will plan and execute experiments, collect data, analyse results, and validate or disprove hypotheses to direct research impact. Collaborate with external industry partners to drive hardware proof-of-concept development and production roadmaps. Write, create, and deliver technical presentations for internal and external stakeholders to disseminate research outcomes and deliver strategic insights across the business. Mentor and grow team capabilities within a multidisciplinary team by coaching research interns and junior researchers, and fostering shared expertise across compute, networking, and memory systems. Doctorate (PhD) in electronic engineering, computer architecture, computer systems or related field, or equivalent training and experience in research. At least 3 years related research or equivalent industrial experience. Demonstrated architectural impact and experience in computer system organisation. A demonstrable record working at the interface of different research fields, or multi-disciplinary teams. Ability to operate effectively in a multi-disciplinary environment, collaborating across domains such as hardware, systems, networking, and AI workloads. Excellent communication skills in English, both written and spoken, including the skill to clearly communicate technical results and justify assumptions to diverse technical audiences. A good understanding of techniques, terminology and frameworks for modern large-scale machine learning systems. Experience working on research or industry projects related to AI systems design, architecture modelling and/or simulation. Experience in scale-up networking in AI GPU/xPU systems. Experience in memory systems for AI inference/training infrastructure. Experience in building high-performance LLM inference systems using SGLang or vLLM. Publications in top computer architecture, systems, and/or ML conferences.