Job description

Overview The Foundry and Inference Training (FIT) team within Microsoft AI Infrastructure builds the platforms, systems, and operating mechanisms that enable teams to develop, train, deploy, and run AI services at scale. The FIT Infrastructure team builds storage and accelerated-compute platforms for large-scale AI training and inference on Azure. You will work across distributed storage, Kubernetes, GPU clusters, networking, and cloud-scale systems to deliver secure, reliable, high-performance, and cost-efficient infrastructure. As a Principal Software Engineer, Storage Infrastructure, you will design, build, and operate storage platforms for foundation-model pre-training, post-training, fine-tuning, and inference. You will partner with Azure infrastructure, networking, operating systems, and AI platform teams to solve challenging problems in storage performance, reliability, scalability, and developer productivity. This role offers opportunities to shape platform architecture, deepen expertise in distributed systems, and grow your technical leadership through broad cross-team impact. Microsoft’s mission is to empower every person and every organization on the planet to achieve more, and we’re dedicated to this mission across every aspect of our company. Our culture is centered on embracing a growth mindset and encouraging teams and leaders to bring their best each day. Join us and help shape the future of the world. Responsibilities Responsibilities: • Design, build, and evolve distributed storage systems and file systems for cloud-scale AI training and inference workloads. • Architect and operationalize storage stacks for large Kubernetes clusters, optimizing data access, throughput, latency, scalability, and reliability for GPU-accelerated workloads. • Design storage and KVkey-value caching architectures that improve LLM inference latency, throughput, resource utilization, and cost efficiency. • Improve durability, availability, performance, and operational maturity through automation, observability, diagnostics, incident response, and resiliency engineering. • Analyze complex cross-layer production issues, identify root causes, and implement durable improvements across storage, compute, networking, and runtime components. • Partner with infrastructure and machine learningLLM platform teams to deliver end-to-end capabilities for foundation-model pre-training, post-training, fine-tuning, and inference. • Lead architecture reviews, influence long-term storage strategy, mentor engineers, and establish engineering practices for secure and reliable cloud services. • Embody our Culture and Values Qualifications Required/minimum qualifications • 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, or Python • OR equivalent experience. Other Requirements • Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include, but are not limited to the following specialized security screenings: • Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud Background Check upon hire/transfer and every two years thereafter. Additional or preferred qualifications Additional or preferred qualifications • Master's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python • OR Bachelor's Degree in Computer Science or related technical field AND 12+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python • OR equivalent experience. • 5+ years of experience designing, building, or operating distributed systems, storage systems, file systems, databases, or cloud infrastructure services. • Experience operating highly available, fault-tolerant services in production environments, including troubleshooting complex distributed-system failures. • Experience applying storage concepts such as durability, replication, consistency, performance, and reliability to production system design. • Experience leading technical design reviews or delivering cross-team engineering projects across software, infrastructure, networking, or platform components. • Experience with geo-distributed storage architectures, replication, disaster recovery, business continuity, or regional failover. • Experience designing storage platforms for large-scale Kubernetes clusters supporting AI training or inference. • Experience with high-performance caching or key-value KV caching for LLM inference. • Familiarity with distributed training or inference technologies, including collective communication libraries and model, data, or pipeline parallelism. • Familiarity with high-throughput networking and low-latency communication technologies such as Remote Direct Memory Access (RDMA), InfiniBand, or RDMA over Converged Ethernet (RoCE). • Familiarity with GPU-accelerated environments, including CUDA, GPU drivers, Kubernetes device plugins, or container runtime integration. • Experience using machine learning or AI techniques for anomaly detection, forecasting, incident management, capacity planning, or operational automation. #AIINFRA Software Engineering IC5 - The typical base pay range for this role across the U.S. is USD $142,800 - $274,800 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $188,000 - $304,200 per year. Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: careers.microsoft.com This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled. Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.

Requirements

Must-haves

The Foundry and Inference Training (FIT) team within Microsoft AI Infrastructure builds the platforms, systems, and operating mechanisms that enable teams to develop, train, deploy, and run AI services at scale.
Design, build, and evolve distributed storage systems and file systems for cloud-scale AI training and inference workloads.
Architect and operationalize storage stacks for large Kubernetes clusters, optimizing data access, throughput, latency, scalability, and reliability for GPU-accelerated workloads.
Design storage and KVkey-value caching architectures that improve LLM inference latency, throughput, resource utilization, and cost efficiency.
Improve durability, availability, performance, and operational maturity through automation, observability, diagnostics, incident response, and resiliency engineering.
Analyze complex cross-layer production issues, identify root causes, and implement durable improvements across storage, compute, networking, and runtime components.
Partner with infrastructure and machine learningLLM platform teams to deliver end-to-end capabilities for foundation-model pre-training, post-training, fine-tuning, and inference.
Lead architecture reviews, influence long-term storage strategy, mentor engineers, and establish engineering practices for secure and reliable cloud services.
Embody our Culture and Values
Required/minimum qualifications
Other Requirements
OR equivalent experience.
5+ years of experience designing, building, or operating distributed systems, storage systems, file systems, databases, or cloud infrastructure services.
Experience operating highly available, fault-tolerant services in production environments, including troubleshooting complex distributed-system failures.
Experience applying storage concepts such as durability, replication, consistency, performance, and reliability to production system design.
Experience leading technical design reviews or delivering cross-team engineering projects across software, infrastructure, networking, or platform components.
Experience with geo-distributed storage architectures, replication, disaster recovery, business continuity, or regional failover.
Experience designing storage platforms for large-scale Kubernetes clusters supporting AI training or inference.
Experience with high-performance caching or key-value KV caching for LLM inference.
Familiarity with distributed training or inference technologies, including collective communication libraries and model, data, or pipeline parallelism.
Familiarity with high-throughput networking and low-latency communication technologies such as Remote Direct Memory Access (RDMA), InfiniBand, or RDMA over Converged Ethernet (RoCE).
Familiarity with GPU-accelerated environments, including CUDA, GPU drivers, Kubernetes device plugins, or container runtime integration.

Nice-to-haves

Additional or preferred qualifications
Additional or preferred qualifications

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aiazurekubernetesllmjavajavascriptpythonmachine learning

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Effort: medium