Principal AI Hardware Architect

Do you want to be at the forefront of innovating the latest hardware designs to propel Microsoft’s cloud growth? Are you seeking a unique career opportunity that combines technical capabilities, cross-team collaboration, with business insight and strategy?

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees, we come together with a growth mindset, innovate to empower others, and collaborate to achieve our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond. In alignment with our Microsoft values, we are committed to cultivating an inclusive work environment for all employees to positively impact our culture every day.

Join the Systems Planning and Architecture (SPARC) team within Microsoft’s Azure Hardware Systems and Infrastructure (AHSI) organization, the team behind Microsoft’s expanding Cloud Infrastructure and for powering Microsoft’s “Intelligent Cloud” mission. Microsoft delivers more than 200 online services to more than one billion individuals worldwide, and AHSI is the team behind our expanding cloud infrastructure. We deliver the core infrastructure and foundational technologies for Microsoft's cloud businesses including Microsoft Azure, Bing, MSN, Office 365, OneDrive, Skype, Teams and Xbox Live.

We are seeking a Principal AI Hardware Architect to join the AI Systems Architecture (ASA) group, where we define and optimize next-generation AI accelerator platforms and large-scale AI systems. In this role, you will drive analytical performance modeling, workload characterization, profiling, and end-to-end performance analysis across GPU and accelerator architectures, working across hardware, software, and system boundaries.

Responsibilities

• Lead performance analysis, profiling, benchmarking, and analytical modeling across GPU and AI accelerator architectures, identifying bottlenecks, architectural trade-offs, and optimization opportunities across hardware, software, and system layers.

• Analyze end-to-end AI workloads and serving systems, including model execution, runtime behavior, memory systems, communication collectives, and workload mapping strategies to understand performance, scalability, efficiency, and cost drivers.

• Develop performance, efficiency, and system-level models to evaluate new architectural features, memory and interconnect innovations, collective communication mechanisms, and accelerator design choices, driving perf/W and TCO optimization.