Principal Software Engineer

Microsoft is a company where passionate innovators come to collaborate, envision what can be and take their careers further. This is a world of more possibilities, more innovation, more openness, and the sky is the limit thinking in a cloud-enabled world. 
 
Microsoft’s Azure Data engineering team is leading the transformation of analytics in the world of data with products like databases, data integration, big data analytics, messaging & real-time analytics, and business intelligence. The products our portfolio include Microsoft Fabric, Azure SQL DB, Azure Cosmos DB, Azure PostgreSQL, Azure Data Factory, Azure Synapse Analytics, Azure Service Bus, Azure Event Grid, and Power BI. Our mission is to build the data platform for the age of AI, powering a new class of data-first applications and driving a data culture. 

​​Within Azure Data, the databases team builds and maintains Microsoft's operational Database systems. We store and manage data in a structured way to enable multitude of applications across various industries. We are on a journey to enable developer friendly, mission-critical, AI enabled operational Databases across relational, non-relational and OSS offerings.​ 

​​Are you passionate about advancing database systems to support the next generation of AI-powered applications? Do you thrive on building deep infrastructure like vector indexing, semantic search, and graph-aware query engines inside a trusted open-source database? Join us as we extend PostgreSQL to natively support AI workloads, combining strong systems engineering with meaningful developer impact. 
 
The Azure PostgreSQL team is building advanced, cloud-native capabilities that extend PostgreSQL into a trusted platform for AI workloads. This includes native support for vector search, semantic indexing, graph queries, and in-database model integration. We are looking for a Principal Engineer to lead foundational systems work that enables intelligent applications to run directly inside PostgreSQL with performance, reliability, and scale. 
 
This is a high-impact individual contributor role where you will define technical direction, influence platform strategy, and work across teams to build durable, scalable, and production-ready AI-native capabilities into PostgreSQL. 
 
About the Team 
 
We are a fast-moving, collaborative team focused on extending PostgreSQL to support AI workloads natively. Our work includes vector indexing, semantic query processing, graph data models, and model-aware execution capabilities, all built directly on top of the PostgreSQL engine. We operate at the intersection of distributed systems, database internals, and AI infrastructure, with a clear emphasis on technical rigor and real-world impact. Our culture values curiosity, deep systems thinking, and inclusive collaboration. We believe meaningful ideas emerge from engineering craftsmanship, open experimentation, and a shared commitment to excellence. 

We do not just value differences or different perspectives. We seek them out and invite them in so we can tap into the collective power of everyone in the company. As a result, our customers are better served. 

• ​​Design and lead the development of core AI capabilities in PostgreSQL including vector indexing, approximate nearest neighbor search, semantic query operators, and graph-native features. 
  
• Architect in-database support for embedding pipelines and model integration to enable retrieval, reasoning, and inference. 
  
• Lead system-level design efforts that span the PostgreSQL engine, extension frameworks, storage abstractions, and control plane surfaces. 
  
• Collaborate with product managers, applied AI researchers, and platform teams to define use cases and translate them into scalable and intuitive capabilities. 
  
• Contribute to production-grade implementation of complex systems, ensuring performance, reliability, and operability. 
  
• Set technical direction and engineering quality standards through code and design reviews, prototyping, and mentorship. 
  
• Act as a technical connector across teams, driving alignment on design, extensibility patterns, and developer experience. 
  
• Stay current on trends in vector databases, graph systems, and AI workloads, applying academic and open-source innovation to real-world engineering.