{"id":2202295,"url":"https://alion.io/job/microsoft-principal-software-engineer-storage-infrastructure","title":"Principal Software Engineer, Storage Infrastructure","company":{"id":18,"name":"Microsoft","domain":"microsoft.com","url":"https://alion.io/company/microsoft","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Eightfold","truth_index":{"grade":"B","score":75,"open_postings":85,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-10T05:45:15Z"}},"role":"Backend","role_family":"Backend","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Redmond, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":142800,"max":274800,"currency":"USD","period":"year","gross":null,"usd_annual":274800},"salary_estimate":null,"experience_years_min":12,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Azure","optional":false},{"name":"C#","optional":false},{"name":"C++","optional":false},{"name":"Fine-tuning","optional":false},{"name":"JavaScript","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"Post-training","optional":false},{"name":"Pre-training","optional":false},{"name":"Python","optional":false},{"name":"CUDA","optional":true},{"name":"CUDA Toolkit","optional":true},{"name":"Incident Management","optional":true},{"name":"InfiniBand","optional":true},{"name":"Machine Learning","optional":true}],"status":"live","first_seen_at":"2026-10-05T20:22:31Z","employer_posted_date":"2026-10-09","last_verified_at":"2026-10-11T18:51:13Z","board_verified":true,"closed_at":null,"days_open":6,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":6},"description":"OverviewThe 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.\nThe 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.\nAs 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.\nMicrosoft’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.\nResponsibilities\nResponsibilities:\nDesign, build, and evolve distributed storage systems and file systems for cloud-scale AI training and inference workloads.\nArchitect and operationalize storage stacks for large Kubernetes clusters, optimizing data access, throughput, latency, scalability, and reliability for GPU-accelerated workloads.\nDesign storage and KVkey-value caching architectures that improve LLM inference latency, throughput, resource utilization, and cost efficiency.\nImprove durability, availability, performance, and operational maturity through automation, observability, diagnostics, incident response, and resiliency engineering.\nAnalyze complex cross-layer production issues, identify root causes, and implement durable improvements across storage, compute, networking, and runtime components.\nPartner with infrastructure and machine learningLLM platform teams to deliver end-to-end capabilities for foundation-model pre-training, post-training, fine-tuning, and inference.\nLead architecture reviews, influence long-term storage strategy, mentor engineers, and establish engineering practices for secure and reliable cloud services.\nEmbody our Culture and Values \nQualifications\nRequired/minimum qualifications\nBachelor'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 PythonOR equivalent experience.\n\nOther Requirements\n\nAbility 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\n\nAdditional or preferred qualifications\nMaster'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 PythonOR 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\nOR equivalent experience.\n\n5+ years of experience designing, building, or operating distributed systems, storage systems, file systems, databases, or cloud infrastructure services.\nExperience operating highly available, fault-tolerant services in production environments, including troubleshooting complex distributed-system failures.\nExperience applying storage concepts such as durability, replication, consistency, performance, and reliability to production system design.\nExperience leading technical design reviews or delivering cross-team engineering projects across software, infrastructure, networking, or platform components.\nExperience with geo-distributed storage architectures, replication, disaster recovery, business continuity, or regional failover.\nExperience designing storage platforms for large-scale Kubernetes clusters supporting AI training or inference.\nExperience with high-performance caching or key-value KV caching for LLM inference.\nFamiliarity with distributed training or inference technologies, including collective communication libraries and model, data, or pipeline parallelism.\nFamiliarity with high-throughput networking and low-latency communication technologies such as Remote Direct Memory Access (RDMA), InfiniBand, or RDMA over Converged Ethernet (RoCE).\nFamiliarity with GPU-accelerated environments, including CUDA, GPU drivers, Kubernetes device plugins, or container runtime integration.\nExperience using machine learning or AI techniques for anomaly detection, forecasting, incident management, capacity planning, or operational automation.\nSoftware 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.\nCertain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:\nhttps://careers.microsoft.com/us/en/us-corporate-pay\nThis position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.\nMicrosoft 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. 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