Join Microsoft's Azure AI Infrastructure team as a Principal Software Engineer and help build the world's largest deep-learning infrastructure service. You will develop cutting-edge components for the Azure ML platform, collaborating with top engineering talent across Azure. Your work will focus on cluster orchestration, job scheduling, storage, networking, and containerization to power AI languages and runtimes, enabling distributed deep learning training and inferencing.

This role involves building and maintaining infrastructure for highly available and scalable Microsoft Service Fabric and Kubernetes clusters. You will lead development, customer support, establish architectural guidelines, and uphold a high standard of service excellence. If you have a proven history of delivering engineering and service excellence on mid-to-large scale services, this is an exciting opportunity.

Architect, design, and develop AI Infrastructure services for large-scale AI training and inferencing. Develop, test, and maintain backend services using C#, Go, Rust, or C++, hosted on Kubernetes. Enhance systems and applications for robustness, efficiency, maintainability, low latency, and cloud security. Provide operational support and take on DRI responsibilities for the product. Cultivate a deep understanding of machine learning systems and customer usage. Collaborate with engineers, data scientists, and external enterprises to build innovative solutions. Offer vision, expertise, and technical leadership to team members and contribute to talent growth.

A Bachelor's or Master's degree in Computer Science or a related field is required.

A minimum of 10 years of coding experience in C#, C, C++, Rust, or Go is essential.

Demonstrated experience with the Linux operating system, Kubernetes, Networking, SDN, and Kubernetes cluster orchestration is necessary.

Proven experience in enhancing service operations or foundational engineering principles is expected.

Strong collaboration skills are a must.

Experience in building and delivering production software or services is required.

Preferred qualifications include a Master's Degree or Ph.D. in Computer Science or a related technical field, coupled with 8+ years of engineering experience in languages such as C, C++, C#, Java, JavaScript, or Python. Experience in the Kubernetes ecosystem, cloud infrastructure (compute, networking, storage, identity), Kubernetes Networking, SDN, large-scale cloud services, distributed systems, or operating systems is highly valued.