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.