Microsoft Research Cambridge

Machine Intelligence

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KBLaM blog | A flowchart illustrating the process of handling a prompt using a language model. The process begins with documents being used to construct and summarize a knowledge base (KB) offline. The summarized KB is then encoded and fed into the main process. A prompt goes through a tokenizer, followed by rectangular attention, and then into the large language model (LLM). The LLM retrieves information from the encoded KB to generate an answer.

Introducing KBLaM: Bringing plug-and-play external knowledge to LLMs

Layered image of Phi Silica, a state-of-the-art small language model integrated into Windows 11 Copilot+PCs

Phi Silica, small but mighty on-device SLM

A Ladder of Reasoning: Testing the power of imagination in LLMs

招聘岗位

Senior Machine Learning Research Engineer – Research Engineering – MSR Cambridge

Posted: 2026年8月5日

地点: Microsoft Research Lab – Cambridge

研究领域: Artificial intelligence, Data platforms and analytics, Programming languages and software engineering

We're seeking a hands-on ML Research Engineer to accelerate our Machine Intelligence research area. You work confidently across training, fine-tuning, inference and evaluation, at single- and multi-GPU scale, with strong…