Senior Staff Engineer - AI Data Path

DataDirect Networks (DDN) is a global leader in AI and multi-cloud data management at scale. They are seeking a highly experienced Senior Staff Engineer specializing in AI Data Path & Storage to lead the development and integration of advanced storage systems with AI inference pipelines, focusing on high-performance data movement architectures.

Responsibilities:

• 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

Requirements:

• 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
• 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