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At MSR Cambridge we are shaping the future of AI infrastructure by tackling ambitious, long-horizon systems challenges that will define the next generation of AI platforms. Our team explores the full stack from models and systems to software and hardware, while working closely with product teams across Microsoft to translate research breakthroughs into impact at scale. The Future AI infrastructure (FAI) team is seeking a Postdoctoral Researcher to pursue foundational research on agentic AI systems. The research emphasis will be on multiagent system designs for scalable agentic workloads with ML and systems techniques for efficient memory, communication, and orchestration of heterogeneous agents. This role is a 2 year fixed term contract and will suit candidates excited by open-ended research questions at the intersection of machine learning, systems, and next-generation AI platforms. FAI team’s proven record of breakthroughs (see AOC and MOSAIC), provides a strong pathway for your research to inform and shape future AI system designs in partnership with the broader MSR teams and Microsoft product teams.
Job Responsibility:
Conduct original research on the design, architecture, and optimization of agentic AI systems, focusing on memory, communication, and orchestration
Prototype new components for multiagent inference with system-level optimizations (e.g. shared latent memory/KV-cache, agent-level parallelism) using relevant framework tools and inference backends like vLLM and SGLang
Explore ML & systems codesign opportunities, such as aligning model capabilities with systems constraints, hardware characteristics, and orchestration strategies, and using Pytorch and other relevant tools of LLM fine-tuning on GPU clusters
Evaluate proposed ideas through real-system experiments, large-scale benchmark evaluation, and empirical studies on real workloads
Work closely with a multidisciplinary team to address both fundamental and applied research challenges
Communicate results clearly, sharing insights with the wider team and partner groups
Contribute to an open, multidisciplinary research environment
Requirements:
PhD (or near completion) in Computer Science, Machine Learning, Electrical Engineering, or a related field
Strong background in ML-systems co-design, AI inference systems, or machine learning systems
Demonstrated ability to conduct independent, high-impact research, evidenced by publications, open-source systems, or deployed artifacts
Ability to work effectively in collaborative, cross-disciplinary research teams
Nice to have:
Familiarity with modern agentic systems, orchestration patterns, or large-scale ML infrastructure
Experience in model post-training, reinforcement learning / evolution strategies, or supervised fine-tuning
Experience in building high-performance LLM inference systems using SGLang or vLLM