Senior Staff Engineer - AI Data Path

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.