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The CoreAI Infrastructure team builds the foundational accelerated compute platforms that power largescale AI training and inference across Azure. Our mission is to deliver secure, reliable, and highly efficient GPU and CPU infrastructure that enables multitenant AI systems at global scale while maximizing utilization, performance, and developer productivity. This role sits at the intersection of cloud infrastructure, systems software, virtualization, and container platforms, working closely with CoreAI, Azure Infrastructure, OS, Networking, and Hardware teams to deliver end-to-end platform capabilities. 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.
Job Responsibility
Design and build GPU and CPU accelerated infrastructure for training and inference workloads, spanning bare metal, virtual machines, and containerized environments with focus on observability key metrics at scale
Develop End to End Observability operational excellence systems for GPU/CPU device management, scheduling, isolation, and sharing (e.g., partial GPU allocation, multitenant usage)
Build and operate advanced orchestration and resource governance and management scenarios using platforms such as AKS, Dynamic Resource Allocation (DRA), and related Kubernetes ecosystem capabilities to enable fair sharing, isolation, and efficient utilization of accelerated resources
Build and evolve virtualization and container stacks to support modern AI workloads, including secure and confidential compute scenarios
Optimize performance, reliability, and utilization across large GPU/CPU fleets, including scaleup and scale out configurations
Partner with networking and storage teams to enable high performance interconnects (e.g., RDMA/InfiniBand class networking) for distributed workloads
Drive end-to-end platform features from design through production, including observability, diagnostics, and operational excellence
Influence platform architecture and technical direction across teams through design reviews and technical leadership
Requirements
Bachelor's Degree in Computer Science or related technical field and 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, Python or equivalent experience
Proven ability to design and operate largescale, production infrastructure with high reliability and performance requirements using Azure Kubernetes Service (AKS)
Strong problem-solving skills and the ability to debug complex, cross layer systems issues
Demonstrated technical leadership, including mentoring engineers and driving cross team architectural alignment
Strong collaboration and communication skills, with the ability to work across organizational boundaries
Expertise with distributed observability technologies (e.g., Prometheus, OpenTelemetry, Grafana) and experience designing or scaling telemetry pipelines for high-throughput production systems
Advanced, hands-on experience with production ML systems, large-scale training infrastructure, NCCL, CUDA libraries and tools