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Microsoft 365 is the fastest growing cloud services business in Microsoft. M365 brings together cloud versions of our most trusted communications and collaboration products like Exchange, SharePoint, OneDrive and Skype with the desktop suite of Office products in an always up-to-date integrated service offering. CapSense is the capacity planning and decisioning platform under the Microsoft 365 Core organization. We are building the foundation to ensure M365 has the right infrastructure, in the right place, at the right time to power Microsoft 365 and Substrate experience—from Exchange and Teams to Copilot and future AI driven workloads. We build large scale systems that transform complex demand, supply, lifecycle, and operational signals into actionable plans and automated decisions that directly influence multi-billion-dollar infrastructure investments. Our mission is to build the reliable, intelligent, and scalable capacity planning and allocation platforms that improve predictability, utilization, and cost efficiency across the fleet. We are looking for a Senior Software Engineer to lead the critical software solutions, with a strong focus on automation, data driven decisioning, and AI assisted planning. This role combines deep engineering fundamentals with architectural ownership, technical leadership, and a forward-looking mindset toward applying AI responsibly at hyperscale.
Job Responsibility:
Own the design and delivery of high impactful services and solutions that automate and scale capacity planning, forecasting, and allocation workflows
Lead architecture and technical decision-making for distributed systems handling high-volume, high-critical data used to drive infrastructure planning and investment decisions
Innovate AI forward systems using AI assisted insights into planning and operational workflows while maintaining correctness, explainability, and trust
Drive end-to-end engineering excellence including reliability, data quality, observability, and operational readiness for business-critical services
Collaborate closely with product managers, data scientists, and partner engineering teams to translate ambiguous business and capacity problems into well-defined technical solutions
Lead and collaborate within a feature crew, mentoring engineers, and raising the overall engineering bar
Influence platform strategy and drive transitions to AI, identify opportunities to reduce manual toil, increase automation, improve predictability across the capacity lifecycle, and transition the capacity planning software solutions with the latest AI technologies
Operate with ownership and autonomy, balancing near term delivery with long term platform health and scalability
Requirements:
Bachelor's Degree in Computer Science or related technical field AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience
Ability to meet Microsoft, customer and/or government security screening requirements are required for this role
Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter
Nice to have:
Master'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 Bachelor'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 equivalent experience
4+ years of experience with distributed systems, backend services, and data intensive applications
Demonstrated ability to own system architecture, make sound technical trade‑offs, and deliver resilient, maintainable solutions
Experience in working with cloud platforms (Azure preferred) and operating services in production
Experience in building capacity planning, forecasting, optimization, or decision support systems
Background in data platforms, analytics pipelines, or ML/AI enabled systems, including model integration and lifecycle considerations
Familiarity with AI assisted engineering patterns, such as using LLMs or predictive models to augment decision making and operational workflows