Principal Software Engineer--M365 Storage Team
We're seeking a highly technical Principal Software Engineer to drive the next generation of AI-native architecture across critical storage, database, and distributed system components. System Architecture & Platform Leadership Lead the architecture, design, and evolution of large-scale distributed storage, database, and data platform systems. Drive modernization of critical platform components to support next-generation AI and Copilot workloads. Re-architect legacy services and infrastructure using AI-native design principles. Define long-term technical strategy and architectural direction across multiple services and teams. Identify and eliminate architectural bottlenecks impacting scalability, reliability, performance, and operational efficiency. Systems Programming & Performance Engineering Design and implement highly efficient systems-level software in languages such as C++, Rust, C#, Go, or similar. Drive end-to-end performance optimization across storage engines, networking, caching, concurrency control, and data access layers. Solve complex challenges involving high concurrency, low latency, throughput optimization, and resource efficiency. Lead root-cause analysis and resolution of difficult production issues involving distributed systems and storage infrastructure. Establish engineering standards and best practices for performance-critical software development. Storage & Database Innovation Design and optimize storage engines, database architectures, replication technologies, indexing systems, and data management frameworks. Lead innovation in areas such as: High availability and disaster recovery Data durability and consistency Replication and synchronization Metadata management Query optimization Storage efficiency and cost optimization Intelligent caching and tiering Drive architectural improvements that enable large-scale AI and retrieval-driven workloads. AI-Native Transformation Champion AI-native engineering practices across architecture, development, testing, and operations. Apply AI-assisted approaches to system design, performance analysis, reliability engineering, and operational automation. Identify opportunities to redesign products and platforms around emerging AI capabilities instead of incrementally enhancing legacy models. Build frameworks and workflows that integrate AI into engineering decision-making and operational management. Influence the organization’s AI transformation strategy through thought leadership and technical innovation. Qualifications 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++, C#, Java, JavaScript, or Python or equivalent experience. Extensive industry experience building large-scale cloud services, distributed systems, storage platforms, or database systems. Solid understanding of operating systems, memory management, threading, synchronization, networking, and I/O subsystems. Expert knowledge of distributed system design and architecture. Proven experience designing or operating large-scale storage and database platforms. Solid understanding of storage engines, database internals, replication mechanisms, transaction processing, consistency models, fault tolerance, high availability architectures, and data durability strategies. Preferred Qualifications 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++, C#, Java, JavaScript, 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++, C#, Java, JavaScript, or Python OR equivalent experience. Experience building storage or database systems supporting AI, search, retrieval, vector, or large-scale analytics workloads. Expertise in cloud-scale platforms such as Azure, AWS, or Google Cloud. Experience with modern database technologies (distributed SQL, NoSQL, vector databases, or analytical engines). Deep familiarity with observability, telemetry, reliability engineering, and automated operations.