Staff Engineer, AI System Architect (Hardware)

<div class="content-intro"><p><span style="font-size: 12pt;"><strong>Please Note:</strong></span></p> <p>To provide the best candidate experience amidst our high application volumes, each candidate is limited to 10 applications across all open jobs within a 6-month period.&nbsp;</p> <p><span style="font-size: 12pt;"><strong>Advancing the World’s Technology Together</strong></span></p> <p>Our technology solutions power the tools you use every day--including smartphones, electric vehicles, hyperscale data centers, IoT devices, and so much more. Here, you’ll have an opportunity to be part of a global leader whose innovative designs are pushing the boundaries of what’s possible and powering the future.&nbsp;</p> <p>We believe innovation and growth are driven by an inclusive culture and a diverse workforce. We’re dedicated to empowering people to be their true selves. Together, we’re building a better tomorrow for our employees, customers, partners, and communities.</p></div><p>The Architecture Research Lab (ARL) focuses on addressing fundamental system-level bottlenecks in modern AI, particularly in&nbsp;<strong>memory capacity/bandwidth and system-scale communication</strong>. By leveraging Samsung’s world-class memory technologies, ARL explores and defines next-generation AI system architectures that deliver step-function improvements in performance, efficiency, and scalability. <br>We are seeking a <strong>Senior Staff AI System Architect</strong> who will play a key role in <strong>bridging AI workloads, system architecture, and hardware design</strong>. In this role, you will develop system-level performance models, drive architecture-level design decisions, and propose forward-looking AI system architectures that shape Samsung’s long-term AI platform strategy.</p> <p><strong>Location</strong>: Daily onsite presence at our San Jose office in alignment with our Flexible Work policy</p> <p><strong>What You’ll Do</strong></p> <ul> <li>Conduct <strong>system-level architectural research</strong> for next-generation AI systems, spanning compute, memory, and interconnect/network subsystems. <br>• Develop and maintain <strong>analytical and simulation-based system modeling frameworks</strong> to evaluate AI workloads and identify performance, scalability, and efficiency bottlenecks at rack- and system-scale. <br>• Analyze representative and emerging AI workloads (e.g., LLMs, DLRMs, and future AI models) to derive <strong>architecture requirements and trade-offs</strong> across compute, memory, networking, and power. <br>• Drive <strong>architecture-level design decisions</strong> through quantitative modeling, design