Principal Software Engineer, Storage Infrastructure

Posted Yesterday
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2 Locations
In-Office or Remote
143K-304K Annually
Expert/Leader
Software • Quantum Computing • Metaverse • Infrastructure as a Service (IaaS)
The Role
Design, build, and operate distributed storage and file-system platforms for cloud-scale AI training and inference. Architect storage and caching for Kubernetes and GPU workloads, improve performance, reliability, observability, and resiliency, diagnose cross-layer production issues, and lead architecture reviews. Partner across infrastructure, networking, operating systems, and AI platform teams while mentoring engineers and shaping long-term storage strategy.
Summary Generated by Built In
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:
https://careers.microsoft.com/us/en/us-corporate-pay


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.

Skills Required

  • Bachelor's degree in Computer Science or a related technical field and 6+ years of technical engineering experience involving coding, or equivalent experience.
  • Ability to pass the Microsoft Cloud Background Check upon hire or transfer and every two years thereafter.
  • Master's degree in Computer Science or a related technical field and 8+ years of technical engineering experience involving coding, or equivalent experience.
  • Bachelor's degree in Computer Science or a related technical field and 12+ years of technical engineering experience involving coding, 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 production services and troubleshooting complex distributed-system failures.
  • Experience applying durability, replication, consistency, performance, and reliability concepts to production storage design.
  • Experience leading technical design reviews or delivering cross-team engineering projects across software, infrastructure, networking, or platform components.
  • Experience with geo-distributed storage, 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 or key-value caching for LLM inference.
  • Familiarity with distributed training or inference technologies, collective communication libraries, and model, data, or pipeline parallelism.
  • Familiarity with RDMA, InfiniBand, or RoCE.
  • Familiarity with GPU-accelerated environments, 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.

Microsoft Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Microsoft and has not been reviewed or approved by Microsoft.

  • Fair & Transparent Compensation — Pay is presented as broadly competitive overall, with clear role/level/location variation and an emphasis on using posted ranges and band information for apples-to-apples comparisons.
  • Retirement Support — Retirement benefits are described as a standout, highlighted by a strong 401(k) match structure and immediate vesting, plus additional plan features for tax-advantaged saving.
  • Parental & Family Support — Family-oriented benefits are portrayed as a meaningful strength, with substantial paid parental leave and added supports like back-up care and adoption/surrogacy assistance.

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