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The future of AI is being built not just in software, but in the physics of light. Our Future AI Infrastructure (FAI) team at Microsoft Research Cambridge is pioneering new hardware and system technologies to fundamentally reshape how AI systems scale — breaking through the bandwidth, latency, and energy walls that constrain today's accelerator architectures. You will join a team at the frontier of AI system design, where novel photonic hardware meets the rapidly evolving demands of large-scale AI workloads. Here, your ideas won't stay on paper — you will work alongside world-class researchers and industry partners to translate architectural vision into working silicon and system prototypes that define the next generation of AI infrastructure. As an AI systems architect with expertise in computer architecture, you will be the core member designing and evaluating future AI computer-system architectures that leverage novel optical interconnects and memory technologies for scale-up networking, memory offloading, and memory disaggregation. You will collaborate closely with experts in adjacent disciplines including optics, networking, distributed systems, and compiler optimisation, to innovate and design overall beneficial solutions to large scale AI infrastructure problems. You will build system models and architectural simulations to explore the design space, conduct ML-system co-design to align hardware capabilities with the demands of agentic AI and large-scale inference workloads, and contribute to software system prototypes that validate architectural concepts end-to-end. You will also collaborate closely with external industry partners to drive hardware proof-of-concept development and inform production roadmaps. This opportunity will allow you to shape the architectural blueprint of next-generation AI systems from the ground up, build deep cross-stack expertise spanning photonics, memory systems, networking, and AI workloads, and develop a unique industry network through hands-on collaboration with leading hardware partners. Microsoft's mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.
Job Responsibility
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
Cooperate closely with other discipline experts to steer the overall research program and ensure mutual directional alignment on the most impactful strategic research priorities
Design, model, and prototype future AI systems through an integrated co-design approach
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
Requirements
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
Nice to have
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