Principal Software Engineer--M365 Storage Team

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. 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. Experience with large-scale data platforms supporting mission-critical workloads. Demonstrated success building systems that operate at hyperscale. Experience designing and supporting high-concurrency architectures, low-latency services, and high-throughput systems, with expertise in service resiliency, performance tuning, capacity management, and production operations. Ability to diagnose and resolve problems across complex distributed environments. Solid interest and demonstrated experience applying AI to engineering workflows and product architectures. Ability to critically evaluate emerging AI technologies and identify transformational opportunities. Experience using AI-assisted development, design, diagnostics, automation, or operational workflows. Passion for reimagining platforms and engineering systems through AI-driven innovation. Exceptional analytical and problem-solving skills. Ability to navigate ambiguity and drive clarity in highly complex technical domains. Solid architecture review and design evaluation skills. Proven influence across organizations without direct authority. Excellent communication and collaboration skills with engineers, architects, product leaders, and executives. These requirements include but are not limited to the following specialized security screenings: 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. Contributions to open-source systems, storage technologies, databases, or distributed computing frameworks. Track record of leading major platform transformations with significant business impact. Design and implement highly efficient systems-level software in languages such as C++, Rust, C#, Go, or similar. Establish engineering standards and best practices for performance-critical software development. Design and optimize storage engines, database architectures, replication technologies, indexing systems, and data management frameworks. Apply AI-assisted approaches to system design, performance analysis, reliability engineering, and operational automation. Build frameworks and workflows that integrate AI into engineering decision-making and operational management.