Note: The job is a remote job and is open to candidates in USA. Microsoft is seeking a Principal Software Engineer for its Azure Blob Storage team. The role leads the design and delivery of distributed storage systems optimized for AI and machine-learning workloads, shaping architecture and driving development, testing, deployment, and technical innovation at hyperscale.
Responsibilities
- Design and deliver next-generation distributed storage systems optimized for AI/ML workloads, from training to inferencing
- Provide technical leadership across architecture, development, testing, and deployment at hyperscale
- Drive innovations in scalability, performance, security, and efficiency to meet the demands of zettabyte-scale storage
- Collaborate with cross-functional teams from AI researchers to cloud infrastructure engineers to ensure storage meets the needs of cutting-edge AI workloads
- Mentor and guide other engineers, fostering a culture of technical excellence, innovation, and collaboration
- Continuously evaluate emerging technologies and trends to anticipate the future of AI-native storage
Skills
- Bachelor's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C++, C#, Java, or Python + OR equivalent experience
- Ability to meet Microsoft, customer and/or government security screening requirements are required for this role
- Master's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C++, C#, Java, or Python + OR Bachelor's Degree in Computer Science or related technical field AND 12+ years technical engineering experience with coding in languages including, but not limited to, C++, C#, Java, or Python + OR equivalent experience
- Passion for distributed systems, large-scale storage, and solving complex systems problems
- Storage system internals in areas such as availability, remote replication, durability schemes, and high-performance I/O
- Database internals, indexing and search techniques, caching and data placement strategies
- Metadata and namespace management at scale
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