About The Position

The Azure Data Lake Storage team is on a mission to build the data foundation that powers the Artificial Intelligence (AI) revolution, and we are looking for engineers to help lead the way. We are seeking a Principal Software Engineer to design and build the next generation of intelligent storage systems purpose-built for the most demanding AI and Machine Learning (ML) workloads in the world. The landscape of computing is shifting. Massive foundation models, generative Artificial Intelligence (AI), and real-time inferencing are redefining how data moves, scales, and performs. Azure Data Lake Storage is evolving beyond traditional storage to become an AI-native platform—one that seamlessly supports the unique scale, throughput, and latency demands of modern AI. From high-bandwidth training pipelines to ultra-low latency for model serving and inference, you will help architect systems that fuel innovation at zettabyte scale. This is a rare opportunity to work on one of the largest and most ambitious storage platforms on the planet, in close partnership with leaders in Artificial Intelligence (AI). As a Principal Software Engineer, you will shape system architecture for the next era of AI storage with a focus on multi-region AI, drive technical innovation across design, development, and operations, tackle complex and high-impact engineering challenges at hyperscale, and guide teams to build high-quality solutions. If you are ready to help define how storage powers AI at global scale, join us and help build the infrastructure that will shape the future.

Requirements

  • Proven experience in software engineering and system architecture.
  • Strong understanding of AI and ML workloads.
  • Experience with high-scale storage systems.
  • Ability to drive technical innovation and tackle complex challenges.

Nice To Haves

  • Experience with multi-region AI systems.
  • Familiarity with generative AI and real-time inferencing.

Responsibilities

  • Design and build intelligent storage systems for AI and ML workloads.
  • Architect systems that support high-bandwidth training pipelines and ultra-low latency for model serving.
  • Drive technical innovation across design, development, and operations.
  • Tackle complex engineering challenges at hyperscale.
  • Guide teams to build high-quality solutions.
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