New 
Research Intern - Azure Cloud Server Performance
 Microsoft | |
 United States, Washington, Redmond | |
 Dec 28, 2024 | |
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OverviewResearch Internships at Microsoft provide a dynamic environment for research careers with a network of world-class research labs led by globally-recognized scientists and engineers, who pursue innovation in a range of scientific and technical disciplines to help solve complex challenges in diverse fields, including computing, healthcare, economics, and the environment. We are building new systems to optimize the millions of server nodes underlying the Microsoft Azure cloud. You will be part of a dynamic and collaborative team chartered to understand and improve how hardware and software ingredients come together to form our Azure VM products. Our work includes optimizing fleet performance, competitive comparisons, developing tools that leverage AI, and providing input to hardware selection.
ResponsibilitiesResearch Interns put inquiry and theory into practice. Alongside fellow doctoral candidates and some of the world's best researchers, Research Interns learn, collaborate, and network for life. Research Interns not only advance their own careers, but they also contribute to exciting research and development strides. During the 12-week internship, Research Interns are paired with mentors and expected to collaborate with other Research Interns and researchers, present findings, and contribute to the vibrant life of the community. Research internships are available in all areas of research, and are offered year-round, though they typically begin in the summer. In this role, you will work to understand and classify cloud workload behavior. You will develop usage models based on hardware telemetry and use the data collected to influence benchmark selection and which hardware we put into next generation cloud systems. You will also gain insight into what workloads matter in a hyperscale public cloud and how platform and hardware design choices affect cloud customer experience and operational efficiency. The knowledge you gain will help you create the future of at-scale computing. | |