Senior Staff Engineer – AI Data Path

🕒 May 6

🇺🇸 United States – Remote

⏰ Full Time

🟠 Senior

🧑‍💻 Full-stack Engineer

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DDN

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 1998

🤖 Artificial Intelligence

💰 $10M Funding Round on 2011-06

Artificial Intelligence • Data Center and Cloud Computing • High Performance Computing

DDN is a global leader in AI data intelligence solutions, providing high-performance computing and sophisticated data management technologies. With a focus on accelerating AI deployments and advanced data analytics, DDN's products, including the Data Intelligence Platform and advanced storage systems, serve diverse sectors such as healthcare, financial services, and government. DDN is committed to transforming enterprise data infrastructure to leverage the full potential of AI and drive operational efficiency.

📋 Description

• 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.

🏖️ Benefits

• Dynamic and driven team structure • Engineering excellence opportunities • Mentoring of junior engineers • Opportunity for strong prioritization skills development • Hands-on involvement across various areas