Software Engineer II

Microsoft•Redmond, WA

About The Position

Microsoft’s Substrate is a massively scaled service that forms the backbone of Microsoft 365 offerings and serves more than a billion users. The Observability Intelligent Cloud group is building data-processing and analytics services in Redmond, Washington, to support AI, monitoring, alerting, analytics, automation, and other scenarios across the end-to-end data lifecycle, with low cost and full data governance. The Software Engineer II will work with senior and principal engineers and partner teams to implement, test, deploy, and maintain well-scoped components of large-scale data-platform services. The role contributes to the development and evolution of an AI-powered data platform, including scalable data processing, analytics, workflow automation, and intelligent service operations. The engineer will build technical depth in AI-assisted engineering, data systems, and production service engineering while following Microsoft’s data privacy, responsible AI, security, and compliance standards.

Requirements

Bachelor’s degree in Computer Science or a related technical field and at least 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

Master’s degree in Computer Science or a related technical field and at least 3 years of technical engineering experience coding in languages such as C, C++, C#, Java, JavaScript, or Python; or a bachelor’s degree in a related field and at least 5 years of such experience; or equivalent experience.
Experience designing, building, testing, and operating cloud services, distributed systems, data pipelines, telemetry processing, or large-scale data systems.
Experience with AI, machine learning, LLM-based workflows, data analytics, service intelligence, automation, time-series data, or production engineering systems.
Experience applying quality engineering practices such as unit testing, integration testing, code reviews, observability, secure coding, and operational readiness.
Problem-solving skills and the ability to learn quickly, communicate clearly, and collaborate effectively across teams.
Ability to deliver well-scoped feature work and contribute to small- to medium-sized projects.

Responsibilities

Contribute to data-platform infrastructure, including frameworks, pipelines, service components, APIs, and AI-enabled automation.
Build and improve scalable, high-performance software components that process large datasets with low latency and high availability, under guidance from senior technical owners.
Contribute to AI- and LLM-enabled platform experiences that help teams understand production signals, analyze service data, improve engineering productivity, and make data-driven operational decisions.
Develop reliable, maintainable, well-tested software supporting data processing, analytics, workflow automation, and intelligent service operations.
Apply Microsoft’s data privacy, responsible AI, security, and compliance standards in day-to-day engineering work.
Implement, test, deploy, and maintain well-scoped software components for data-platform infrastructure, frameworks, pipelines, APIs, analytics, and automation.
Work with senior engineers on service components that process large-scale service data, telemetry, metadata, configuration, and workflow outputs used by AI/ML and LLM-enabled experiences.
Contribute to AI-powered engineering and operations capabilities, including data processing, signal analysis, intelligent summarization, workflow automation, service insights, and evidence generation.
Collaborate with engineers within and outside the team to integrate components into larger OIC and Substrate systems, following established design patterns and engineering standards.
Participate in design discussions for assigned features by evaluating implementation options, documenting assumptions, asking questions, and incorporating feedback.
Participate in code reviews as both reviewer and reviewee to improve code quality, reliability, maintainability, and engineering practices.
Improve platform quality and live-site readiness through tests, monitoring, dashboards, diagnostics, documentation, and fixes for recurring operational issues.
Develop technical depth in assigned features while learning AI-assisted engineering, data processing, service reliability, privacy, compliance, and responsible AI practices.
Embody Microsoft’s culture and values.