Senior Researcher - AI Computer Architecture

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.

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.

Responsibilities:

• 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.
• 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.
• 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.
• Build system models and simulations to explore architectural trade-offs in scale-up networking, memory offloading, and memory disaggregation.
• Characterise agentic AI workload demands, and develop software prototypes to validate concepts end-to-end.
• 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.
• Expand relationships with hardware vendors, research teams, and production teams inside and outside Microsoft, contributing to strategic decisions that bridge research to production.
• 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.

Qualifications:

• 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.

Additional or Preferred Qualifications:

• 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.

Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances.