5 days ago
Base Salary
$143k - $304k/yr
Responsibilities
- Design, build, and evolve distributed storage systems and file systems for cloud-scale AI workloads.
- Architect and operate storage stacks for large Kubernetes clusters supporting GPU-accelerated training and inference.
- Design storage and key-value caching architectures to improve LLM inference latency, throughput, utilization, and cost.
- Improve durability, availability, performance, observability, diagnostics, incident response, and resiliency through automation.
- Analyze production issues across storage, compute, networking, and runtime components and implement durable solutions.
- Partner with infrastructure, networking, operating systems, and AI platform teams on end-to-end capabilities.
- Lead architecture reviews, shape storage strategy, mentor engineers, and establish secure and reliable engineering practices.
Requirements
- Bachelor's degree in computer science or a related technical field and 6+ years of technical engineering experience with coding, or equivalent experience.
- Ability to pass Microsoft, customer, and/or government security screening requirements, including the Microsoft Cloud Background Check.
- 5+ years of experience designing, building, or operating distributed systems, storage systems, file systems, databases, or cloud infrastructure services.
- Experience operating highly available and 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 cross-team engineering projects.
- 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 and with GPU-accelerated environments such as 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.
- Preferred qualifications include a master's degree with 8+ years of experience, or a bachelor's degree with 12+ years of experience, or equivalent experience.
Benefits
- The typical U.S. base pay range is USD $142,800-$274,800 per year, with a separate San Francisco Bay Area and New York City metropolitan area range.
- The position may be eligible for benefits and other compensation.
- Applications are accepted on an ongoing basis until the position is filled, with the posting open for a minimum of five days.
Tech Stack
About Microsoft
Microsoft develops operating systems, productivity software, cloud services, developer tools, and consumer devices for individuals, enterprises, and governments. Its main products include Windows, Microsoft 365, Azure, Visual Studio/GitHub, Xbox, and LinkedIn; revenue comes from software subscriptions and licenses, cloud consumption, hardware sales, and advertising. Founded in 1975 and headquartered in Redmond, Washington, Microsoft is a public company traded on Nasdaq.