Senior Software Engineer

The Artificial Intelligence (AI) Infrastructure Engineering Systems team at Microsoft builds the cloud platform, engineering infrastructure, and developer systems that enable teams to develop, deploy, and operate AI services at scale. You will help create foundational capabilities spanning build and release systems, Kubernetes, networking, observability, infrastructure automation, security, and service lifecycle management. Working closely with AI researchers, product engineers, and hardware teams, you will help provide reliable and consistent paths from development to production within a multidisciplinary, collaborative, and inclusive engineering team.

Role Summary

As a Staff Engineer - AI Infrastructure Reliability in the AI Infrastructure Engineering Systems team, you will design, implement, and ensure the reliability and quality of engineering platforms, developer tooling, and Azure integrations. This hands-on technical role covers architecture, design, implementation, testing, deployment, and production operations for large-scale AI training and inference platforms. You will contribute heavily to incident management, automated validation, compute fleet efficiency, observability, and internal tooling that improves developer velocity and system reliability.

This opportunity will allow you to deepen your distributed-systems and cloud-platform knowledge, advance reliability practices for rapidly evolving AI workloads, and grow your technical leadership across multidisciplinary teams.

Microsoft Culture Statement

Microsoft's mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Responsibilities

• Design, build, and optimize components of distributed AI platforms to improve availability, performance, scalability, reliability, and cost efficiency.

• Lead incident-management efforts and engineer technical solutions that systematically reduce Time to Detect (TTD), Time to Mitigate (TTM), and Time to Resolve (TTR) across AI training and inference workloads.

• Build and scale automated validation frameworks and test suites that identify regressions early and maintain system stability.

• Implement graphics processing unit (GPU) and compute fleet optimizations that improve resource utilization, throughput, and operational efficiency at scale.

• Develop platform services, internal tools, and automation that streamline engineering workflows, improve developer velocity, and raise code quality standards.

• Write production code, build telemetry and observability tools, and debug complex system issues to resolve root causes and prevent incident recurrence.

• Provide technical leadership, mentor engineers, and lead multi-team initiatives across infrastructure, security, networking, and service teams.

Preferred Qualifications