Cambridge Residency Programme: Next-Generation AI Datacentre Networking

Track A - Modelling & Simulation

Best suited to candidates whose primary strength is analytical reasoning, performance modelling, or simulation.

• Design and analyse novel network architectures (e.g., hybrid optical-electrical, reconfigurable topologies) tailored for AI communication patterns.
• Develop analytical models and simulators to quantify the performance, cost, and energy trade-offs of proposed designs.
• Study architectural trade-offs involving topology, transport, collective communication, and emerging optical/networking hardware.
• Collaborate with systems researchers to compare model predictions with testbed measurements.
• Evolve existing evaluation tools and frameworks to address new research questions and scenarios relevant to product teams.

Track B - Systems Implementation & Experimental Validation

Best suited to candidates whose primary strength is building and evaluating real systems on experimental platforms.

• Implement and evaluate network protocols, transport mechanisms, and collective communication schemes on experimental hardware testbeds featuring modern GPUs, optical circuit switches, and RDMA interconnects.
• Build and run communication-intensive workloads (e.g., collective algorithm benchmarks, distributed training/inference jobs) to stress-test new network designs.
• Co-design and validate new protocols and algorithms with modelling collaborators.
• Drive experimental validation on the group's testbed and contribute to its continued evolution.
• Expand existing tools and prototypes to address scenarios relevant to both research and product teams.

In both tracks, you will publish findings at top-tier academic venues and contribute to Microsoft's long-term AI infrastructure strategy.

• PhD in Computer Science, Computer Engineering, Electrical Engineering, Applied Mathematics, Operations Research, or a related field.
• Evidence of independent research, such as first-author publications, strong thesis work, or impactful prototypes.
• Ability to communicate research clearly through papers, talks, and cross-functional collaboration.
• Strength in at least one of the following areas:
• Modelling & simulation (Track A): Demonstrated experience in analytical modelling, simulation, or performance evaluation of networks or distributed systems (e.g., queueing models, flow-level simulation, stochastic models, LP-based analysis, or alpha-beta models).
• Systems implementation (Track B): Strong systems programming skills in C++/CUDA/Python, with hands-on experience building or evaluating networked systems, distributed systems, or AI training/inference infrastructure., * Experience with datacentre network architectures, transport protocols, or collective communication.
• Familiarity with circuit-switched or optical networking concepts (e.g., optical circuit switches, co-packaged optics).
• Understanding of AI/ML workload communication patterns (e.g., all-reduce, MoE routing, pipeline parallelism).
• Experience building simulators, evaluation frameworks, or experimental prototypes.
• Proficiency in Python and familiarity with scientific computing libraries (NumPy, SciPy, pandas).
• Experience in one or more of the following systems areas:
• High-performance networking: RDMA (RoCEv2, InfiniBand), transport protocol implementation, or congestion control.
• GPU and distributed ML communication: CUDA programming, NCCL, or experience with ML training/inference systems (e.g., PyTorch, Megatron, vLLM).
• Experimental infrastructure: Building or managing hardware testbeds, measurement and profiling.

This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.

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