Role Description DDN is seeking a highly experienced Senior Staff Engineer specializing in AI Data Path & Storage to lead hands-on development and integration of advanced storage systems with next-generation AI inference pipelines. This role involves coding, prototyping, and rapidly iterating on solutions in close collaboration with architects to design and deliver high-performance data movement architectures. - Leverage NVIDIA’s NIXL (Inference Transfer Library) alongside the Infinia Data Intelligence Platform to enable ultra-low-latency, high-throughput data movement across GPU, memory, and distributed storage layers. - Involve workloads including KV cache management and vector database retrieval. - The ideal candidate brings deep expertise in distributed storage, GPU data paths, and large-scale system optimization, with a proven track record of building and shipping production-grade AI infrastructure. Qualifications - Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field. - 12+ years of experience in storage systems, distributed systems, or performance engineering. - Proven track record of architecting and delivering large-scale, high-performance infrastructure systems. - Deep expertise in distributed storage architectures (object storage, scalable file systems, or cloud-native storage platforms). - Strong understanding of Linux I/O stack, filesystem internals, and storage protocols. - Extensive hands-on experience with NVMe, SSD optimization, and high-performance storage environments. - Strong experience with RDMA, InfiniBand, or other high-speed data transfer technologies. - Solid understanding of GPU computing concepts and CPU–GPU data movement patterns. - Proficiency in Python and/or C/C++, with advanced debugging, profiling, and performance tuning skills. - Demonstrated ability to optimize latency-sensitive, high-throughput production systems. Requirements - Lead the design and implementation of high-performance data movement pipelines using NVIDIA NIXL across GPU, CPU, and storage tiers. - Architect and drive integration of DDN Infinia with GPU-accelerated inference platforms for large-scale, real-time AI workloads. - Own end-to-end optimization of I/O paths between GPU memory and storage using technologies such as NVIDIA GPUDirect Storage, RDMA, and NVMe-over-Fabrics. - Define and implement multi-tier storage architectures (NVMe, SSD, object storage) optimized for inference latency, throughput, and scalability. - Lead development of advanced KV cache management strategies, including offloading, prefetching, and persistence across distributed storage layers. - Partner with AI/ML engineering teams to optimize inference performance in frameworks such as PyTorch and TensorFlow. - Establish benchmarking frameworks and lead performance tuning efforts for storage and data movement in production inference environments. - Diagnose and resolve complex system bottlenecks across storage, networking, and GPU subsystems. - Influence architecture decisions for distributed inference systems, ensuring scalability, resilience, and efficient data locality. - Drive engineering excellence through best practices in observability, performance monitoring, automation, and reliability engineering. - Mentor junior engineers and provide technical leadership across cross-functional teams. Preferred Skills - Hands-on experience with NVIDIA NIXL or similar data movement frameworks. - Experience with GPU-aware storage pipelines and GPUDirect Storage. - Strong understanding of AI inference systems, LLM serving architectures, and KV cache optimization. - Experience with Retrieval-Augmented Generation (RAG) pipelines and open vector search ecosystems. - Background in high-performance computing (HPC) or hyperscale distributed environments. - Expertise in caching strategies, memory tiering, and data locality optimization. - Experience designing disaggregated compute and storage architectures. What You’ll Work On - Leading the evolution of storage systems into GPU-native data layers for AI inference. - Building next-generation distributed AI infrastructure using NIXL and Infinia. - Driving performance breakthroughs in real-time LLM inference at scale. - Designing storage architectures for large-scale AI datasets and retrieval systems.