Principal Performance Architect

Job not on LinkedIn

April 25

Apply Now

Receive Emails with Similar Jobs

DDN

WebsiteLinkedInAll Job Openings

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.

1001 - 5000 employees

Founded 1998

🤖 Artificial Intelligence

💰 $10M Funding Round on 2011-06

📋 Description

• DDN is seeking a Principal Performance Architect to drive end-to-end performance analysis and optimization for Infinia, our AI-native, highly distributed data intelligence platform. • In this critical role, you will own the visibility, measurement, and tuning of system performance across the entire stack — from AI applications, vector databases, and inference pipelines, down to I/O patterns on NVMe drives. • You’ll partner with teams across engineering, architecture, and field engineering to deliver insights and innovations that drive customer value at scale. • This is a hands-on, full-stack systems performance role — ideal for someone who thrives on pushing the boundaries of scale, throughput, and latency across deeply interconnected layers.

🎯 Requirements

• 15+ years of experience in systems performance engineering or low-level infrastructure development. • Proven expertise in end-to-end performance debugging across distributed systems, from application logic down to I/O subsystems. • Strong background in system-level tools (e.g., perf, ftrace, bpftrace, nvme-cli, or similar). • Deep understanding of NVMe, CPU and memory hierarchy, caching, thread scheduling, and Linux kernel internals. • Experience optimizing compute- or data-intensive applications such as AI inference, search, or analytics. • Proficiency in C/C++ or Rust and scripting languages such as Python or Bash. • Excellent written and verbal communication skills, especially around technical reporting and architectural recommendations.