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Principal Software Engineer - Compute Graph Compiler

Microsoft
Redmond, USA
Full-time
Senior · 6+ years experience
Onsite
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Define and drive the technical vision, architecture, and roadmap for key components of the Windows ML compiler stack. Lead cross-team initiatives that improve AI workload performance, scalability, and efficiency across CPU, GPU, and NPU hardware. Drive NPU enablement strategy and advanced optimization techniques for next-generation AI processors. Partner with hardware vendors and internal platform teams to influence future AI hardware and software capabilities. Design and develop high-performance compiler infrastructure, execution engines, and developer tooling. Identify and solve complex systems challenges involving scheduling, memory management, code generation, graph optimization, and hardware abstraction. Mentor engineers and provide technical leadership across multiple projects and organizations. Bachelor's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with production-quality systems software, AI/ML platforms, or related technologies OR equivalent experience Demonstrated experience designing and delivering production-quality systems software, AI/ML platforms or related technologies. Committed hands-on experience with ML compilers, runtimes, frameworks, tools and platform technologies. In-depth operating system development experience, including user-mode and/or kernel-mode driver development. Experience designing ML accelerator or hardware microarchitecture for real-world products, and connecting that hardware with compilers, runtimes, drivers and frameworks. Knowledge of computer architecture, heterogeneous computing, memory systems, parallel processing and hardware/software co-design. Proven experience optimizing ML workloads across the full software and hardware stack, including GPU optimization. A track record of technical leadership: defining architecture and driving complex AI/ML research and development across hardware and software teams. This includes taking AI platforms or accelerators from early design and prototypes through to production.

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