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Research 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. The Azure Hardware and Systems Infrastructure organization is central to defining Microsoft's first-party Artificial Intelligence (AI) infrastructure architecture and strategy. This is a dynamic and fast-paced environment that in close partnership with sister organizations helps define System on Chip (SoC) designs, interconnect topologies, memory hierarchies, and much more, all in the context of enabling and optimizing workload optimized data flows for large-scale AI models. This organization plays a critical role in roadmap definition all the way from concept to silicon to hyperscale integration.
Job Responsibility:
Research 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
As a Research Intern, you will be at the forefront of hardware/software co-design and have a direct impact in answering critical questions around designing an optimized AI system and evaluating real-world impact on the Azure’s supporting hyperscale infrastructure
This role will evaluate opportunities to co-optimize central processing unit (CPU), graphics processing unit (GPU) and networking infrastructure for the Maia accelerator ecosystem
You will be expected to identify system stress points, propose novel architectural ideas, and create methodologies using a combination of workload characterization, modeling and benchmarking to evaluate their effectiveness
Requirements:
Accepted or currently enrolled in a PhD program in Computer Science or related STEM field
At least 1 year of experience with performance analysis tools and methodologies, optimization and modeling
Proficiency with frameworks such as PyTorch, SGLang, Dynamo, and AI accelerator programming models/compilers such as CUDA and Triton
Deep understanding of GPU and AI architectures including memory hierarchies, compute-communication interplay, kernel scheduling and interconnect properties
Familiarity with CPU/server architectures including understanding of PCIe topologies and accelerator/NIC/peripheral demand. Solid understanding of CPU involvement in dispatching, scheduling and orchestration of input data pipelines to AI accelerators
Hands-on experience with benchmarking, profiling, identifying perf bottlenecks and performance analysis and optimization, including trace generation, event monitoring and instrumentation
Familiarity with roofline performance modeling, detailed performance simulations and awareness of speed vs accuracy tradeoffs in various performance modeling methodologies
Ability to apply the appropriate performance analysis methodology including devising new or combinatorial approaches in evaluating complex system architecture what-if scenarios