Principal Software Engineer, Storage Infrastructure

Working with Engineering, Finance, Global Talent, Human Resources (HR), Procurement, and partner organizations, you will help the team align resources and execution with its changing business priorities.Build and manage the storage stack powering the next generation of LLM systems. 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. 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 Additional or preferred qualifications 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. Bachelor's Degree in Computer Science, Computer Engineering, or a related technical field AND 8+ years of technical engineering experience coding in languages including, but not limited to, C, C++, C#, Go, Java, or Python, or Rust; Doctorate (PhD) in Computer Science, Computer Engineering, or a related technical field and 6+ years of technical engineering experience; OR Master's Degree and 8+ years; OR Bachelor's Degree and 12+ years; OR equivalent experience.