The HPC/AI (High performance Computing and Artificial Intelligence) team is on a mission to build the next-generation distributed AI supercomputer, enabling breakthroughs in artificial intelligence by delivering unmatched computational power, scalability and reliability. We design and develop cutting-edge infrastructure that supports high-performance AI model training at scale, laying the foundation for innovations that redefine what AI can achieve. As a Senior Software Engineer on the HPC/AI team, you will play a pivotal role in shaping the next-generation networking infrastructure for AI training and inference in Azure Cloud. This is a unique opportunity to work at the intersection of two of the hottest fields in technology: AI and high-performance computing. With the explosive growth of generative AI and the increasing demand for large-scale, low-latency systems, this area is at the forefront of innovation and impact. You will work across diverse network architectures and cutting-edge processor and accelerator technologies, driving the design and delivery of a comprehensive, end-to-end solution with a relentless focus on performance, scalability, and observability.

Design, develop, and optimize networking solutions tailored for large-scale AI training infrastructure.

Architect and implement high-performance, low-latency, and low-jitter communication frameworks for distributed systems.

Benchmark, analyze, and enhance the scalability and reliability of networking systems to handle petabyte-scale data transfer.

Debug and resolve complex networking issues in large-scale, high-performance environments.

Drive identification of dependencies and the development of design documents for a product, application, service, or platform.

Create, implement, optimize, debug, refactor, and reuse code to establish and improve performance and maintainability, effectiveness, and return on investment (ROI).

Act as a Designated Responsible Individual (DRI) and guides other engineers by developing and following the playbook, working on call to monitor system/product/service for degradation, downtime, or interruptions, alerting stakeholders about status and initiates actions to restore system/product/service for simple and complex problems when appropriate.

Proactively seek new knowledge and adapts to new AI trends, technical solutions, and patterns that will improve the availability, reliability, efficiency, observability, and performance of products while also driving consistency in monitoring and operations at scale.