About the role
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. Support development and troubleshooting from the frontline, resolving complex issues impacting large-scale services. Bachelor's or master's degree in computer science or a related field. 3+ years designing, developing, and shipping high quality software. 2+ years of experience with distributed systems and cloud-based infrastructure. 1+ year of experience with DevOps practices (CI/CD, automated testing, deployment, etc.). Passionate and self-motivated. Strong ability in self-learning, entering new domain, managing through uncertainty in an innovative team environment. 6+ years of software development experience in C#, C++, Python, or similar languages. 3+ years of experience with containerization tools (e.g., Docker, Kubernetes). Knowledge and hands on experience with production ML systems, large-scale training infrastructure, NCCL, CUDA libraries and tools. This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled. *
