Senior Software Engineer

CoreAI is at the forefront of Microsoft’s mission to redefine how software is built and experienced. We are responsible for building the foundational platforms, services, programming models, and developer experiences that power the next generation of applications using Generative AI. Our work enables developers and enterprises to harness the full potential of AI to create intelligent, adaptive, and transformative software.

The AI Core Infrastructure team, part of AI Platform team in CoreAI Organization is responsible for large-scale, highly reliable and efficient GPU management infrastructure and the inference and training platforms that powers all of Microsoft’s AI workloads, such as M365 CoPilot, Github CoPilot, Microsoft CoPilot, AI Foundry’s Inference and Fine-Tuning offering of OAI and OSS models, and many more.

As a Senior Software Engineer on the fleet management team, you will work on cutting edge infrastructure and tools to build manage and support large scale training and inference clusters of latest generation of NVIDIA and AMD GPUs in Azure and Microsoft partner clouds on some of the world’s largest AI Supercomputers.

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.

In alignment with our Microsoft values, we are committed to cultivating an inclusive work environment for all employees to positively impact our culture every day.

Responsibilities

As a senior engineer on the infrastructure fleet management team, your responsibilities include:

- Architect, design, and develop core AI Infrastructure services developed in Go, Rust, Python, C++, and C# deployed on large-scale Kubernetes clusters to support state-of-the-art LLM training and inference.

- Design, build, and manage large-scale GPU clusters to support LLM training, and inference workloads.

- Enhance systems and applications to deliver high stability, low latency, strong security, and maintainability in large-scale complex training environments in Azure and in partner clouds.

- Provide operational support, technical leadership, and vision while contributing to the deployment, monitoring, and continuous improvement of engineering systems and practices.