Infrastructure Engineer

About Microsoft AI and the Infrastructure Engineer Role

As Microsoft continues to push the boundaries of AI, the Infrastructure Engineer will join a passionate team tackling the most interesting and challenging AI questions of our time. Microsoft AI envisions building systems with true artificial intelligence across agents, applications, services, and infrastructure, aiming to make AI accessible to all consumers, businesses, and developers.

The Platform Infrastructure team is crucial, responsible for building and scaling the backend platform at the core of Microsoft consumer products, integrating with AI models, and providing essential tools for engineers. This role involves close collaboration with cross-functional engineering, product management, and AI research teams to empower Microsoft Copilot teams to bring cutting-edge AI research to production effectively.

Microsoft seeks experienced Platform Infrastructure Engineers who are passionate about AI, deeply proficient in scaling backend technologies, and master templating to architect future-proof solutions.

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. The company fosters a culture of growth mindset, innovation, collaboration, respect, integrity, and accountability, creating an inclusive environment where everyone can thrive.

This U.S. Mountain View, CA position requires local presence in the San Francisco area and in-office work 3 days a week. Starting January 26, 2026, MAI employees within 50 miles of a designated Microsoft office are expected to work from the office at least four days a week.

Responsibilities of an Infrastructure Engineer

• Design, develop, and maintain performant and secure AI Platform services powering Copilot.
• Collaborate with platform, infrastructure, application engineers, and AI researchers on next-generation AI products.
• Ship high-quality, maintainable code, ensuring reliability, scalability, and performance of platform components.
• Overcome roadblocks to deliver work quickly and iteratively to users.
• Thrive in a fast-paced, design-driven, product development cycle.
• Embody Microsoft's Culture and Values.

Required Qualifications

• Bachelor’s degree in Computer Science or related technical discipline AND 4+ years of technical engineering experience building services and products in languages such as Python, C#, C++, Rust, Java; OR equivalent experience.
• 4+ years’ experience building scalable platforms on public cloud infrastructure (Azure, AWS, GCP) with extensive use of technologies like Docker, Kubernetes, nginx, RDBMS, key-value stores.
• 4+ years’ experience in building and releasing production software at the platform level.
• Solid knowledge of APIs, data flows, systems, and services.

Preferred Qualifications

• Experience managing high-scale, multi-region, production environments on Kubernetes in cloud environments.
• Ability to identify, analyze, and resolve complex technical issues, ensuring optimal performance, scalability, and user experience.
• Dedication to writing clean, maintainable, and well-documented code with a focus on reliability, security, and ease of use.
• Demonstrated interpersonal skills and ability to work closely with cross-functional teams, including product managers and other engineers.
• Ability to clearly communicate complex technical concepts to both technical and non-technical stakeholders.
• Ability to work in a fast-paced environment, manage multiple priorities, and adapt to changing requirements and deadlines.
• Passion for learning new technologies and staying up to date with industry trends, best practices, and emerging technologies in web development and AI.
• Proven ability to collaborate and contribute to a positive, inclusive work environment, fostering knowledge sharing and growth within the team.

Key skills/competency

• AI Platform Engineering
• Backend Development
• Cloud Infrastructure
• Kubernetes
• Scalability
• Distributed Systems
• Python/C#/C++/Rust/Java
• API Design
• System Architecture
• Reliability Engineering