Software Engineering

Microsoft•,

2d•$102,100 - $219,200

About The Position

Microsoft’s Cloud Operations + Innovation (CO+I) organization designs, builds, and operates the global infrastructure powering Microsoft cloud services. Its Inventory and Asset Management (IAM) team provides platforms for managing equipment and inventory throughout their operational lifecycles, including receiving, asset and component tracking, location and custody management, reservations, transfers, break-fix, warranty and RMA, Critical Environment inventory, decommissioning, and secure handling of data-bearing devices. The Software Development Engineer II will develop production-grade inventory services, APIs, workflows, and user experiences. The role partners with product and program management, datacenter operations, supply-chain stakeholders, and engineering teams to discover customer and operational problems, define solutions, and deliver them from discovery and design through implementation, validation, rollout, and impact measurement. The engineer is expected to use AI-native approaches and engineering tools responsibly, explore opportunities to incorporate AI and automation into inventory workflows, and maintain ownership of quality, security, reliability, and production operations.

Requirements

Bachelor’s degree in Computer Science or a related technical field and 2+ years of technical engineering experience coding in languages such as C, C++, C#, Java, JavaScript, or Python, or equivalent experience.
Ability to meet Microsoft, customer, and/or government security screening requirements, including passing the Microsoft Cloud background check upon hire or transfer and every two years thereafter.

Nice To Haves

Bachelor’s degree in Computer Science or a related technical field and 4+ years of technical engineering experience; or a Master’s degree in Computer Science or a related technical field and 2+ years of technical engineering experience; or equivalent experience.
Experience designing and delivering production software, developer tools, services, or platform components as part of a collaborative engineering team.
Experience using generative AI models, coding agents, or engineering platforms to build software, with awareness of evaluation, reliability, security, and appropriate human oversight.
Experience improving developer workflows, engineering systems, operating practices, or developer experience, and assessing results using feedback or measurements.
Working knowledge in one or more areas such as cloud platforms, distributed systems, developer tooling, identity and security, observability, CI/CD, testing infrastructure, or safe deployment.
Experience building or operating products for developers, AI-enabled engineering workflows, internal developer platforms, developer tooling, or cloud infrastructure.
Ability to work through moderately ambiguous problems, make sound implementation tradeoffs, and take well-defined features from design through production operation.
Ability to communicate technical ideas clearly, collaborate across team boundaries, learn from feedback, and contribute positively to team engineering practices.

Responsibilities

Seek out meaningful customer and operational problems, challenge assumptions, and focus engineering effort on outcomes.
Partner with product and program management, datacenter operations, supply-chain stakeholders, and partner engineering teams to understand workflows, gather evidence, identify root causes, and define problems.
Own work end to end, from discovery and problem framing through technical design, implementation, validation, production rollout, and measurement of customer and business impact.
Use coding agents, generative AI, automation, and emerging engineering tools to accelerate engineering work while maintaining human judgment and accountability.
Identify and test opportunities to embed AI, agents, and automation into datacenter inventory workflows and shared engineering capabilities, using measurable results to validate value.
Design and build production-grade inventory services, APIs, workflows, and user experiences for asset lifecycle, inventory, component tracking, movement, fulfillment, break-fix, and decommissioning scenarios.
Translate ambiguous business and operational needs into technical plans, interfaces, data contracts, and maintainable implementation patterns.
Make and communicate engineering tradeoffs involving reliability, security, usability, performance, cost, and delivery speed.
Own quality and production readiness, including automated testing, observability, secure data handling, safe deployment, incident response, and continuous improvement.
Use telemetry, customer feedback, operational data, and experiments to evaluate solutions, diagnose issues, and decide whether to scale, redirect, or stop an approach.
Contribute to design and code reviews, share knowledge, and help the team adopt effective AI-native and software-engineering practices.