Microsoft Research Cambridge is hiring two researchers for its Cambridge Residency Programme (two-year postdoctoral positions) to advance the design and evaluation of next-generation datacentre networks for AI workloads. We are seeking to hire a collaborative pair of researchers with complementary profiles: one focused on analytical modelling and simulation, the other on systems implementation and experimental validation.

AI training and inference are fundamentally changing the communication patterns and cost envelope of cloud infrastructure, creating new opportunities to rethink datacentre network architecture and systems from first principles.

The positions are based at Microsoft Research Cambridge within the Future AI Infrastructure group. The group combines long-term research with close collaboration across Microsoft product teams and academic partners, creating opportunities to publish, prototype, and influence future cloud systems. The team spans networking, distributed systems, optics, and AI infrastructure, and has published at venues including SIGCOMM and Nature. As a researcher in this group, you will have access to production-scale data and unique experimental infrastructure, including optical circuit switches and RDMA testbeds.

• Contract Duration: 2 Years
• Location: Cambridge, UK

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.

Required/Minimum Qualifications:  

• 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.

Preferred/Additional Qualifications:

• 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.