Generative AI is transforming how people create, collaborate, and communicate - redefining productivity across Microsoft 365 and our customers globally. At Microsoft, we run the biggest platform for collaboration and productivity in the world with hundreds of millions of consumer/enterprise users. Tackling AI efficiency challenges is crucial for delivering these experiences at scale.

Within our Microsoft wide Systems Innovation initiative, we are working to advance efficiency across AI systems, where we look at novel designs and optimizations across AI stacks: models, AI frameworks, cloud infrastructure, and hardware. We are an Applied Research team driving mid- and long-term product innovations. We closely collaborate with multiple research teams and product groups across the globe who bring a multitude of technical knowledge in cloud systems, machine learning and software engineering. We communicate our research both internally and externally through academic publications, open-source releases, blog posts, patents, and industry conferences. Further, we also collaborate with academic and industry partners to advance the state of the art and target material product impact that will affect 100s of millions of customers.

We are looking for a Senior Researcher - GPU Performance – Hardware/Software Codesign researcher to explore hardware/kernel-level optimizations to deliver significant efficiency gains for Large Language Models and Generative AI experiences.

The qualified candidate will have a solid background in GPU architecture, accelerator design, machine learning, or systems research and the ambition to apply them to large scale production systems. This role combines deep technical knowledge in GPU architecture with practical implementation skills to create efficient, scalable computational kernels. Further, the qualified candidate is expected to demonstrate a history of solving hard technical problems and is motivated to tackle the hardest problems in building a full end-to-end AI stack. An entrepreneurial approach and ability to take initiative and move fast are essential.

Have a look at this link for reading: Efficient AI - Microsoft Research

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