Staff Engineer, AI System Architect (Hardware)

Please Note: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. Advancing the World's Technology TogetherOur 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. 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.The Architecture Research Lab (ARL) focuses on addressing fundamental system-level bottlenecks in modern AI, particularly in memory capacity/bandwidth and system-scale communication. 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. We are seeking a Staff AI System Architect who will play a key role in bridging AI workloads, system architecture, and hardware design. 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.Location: Daily onsite presence at our San Jose office in alignment with our Flexible Work policyWhat You'll DoConduct system-level architectural research for next-generation AI systems, spanning compute, memory, and interconnect/network subsystems. • Develop and maintain analytical and simulation-based system modeling frameworks to evaluate AI workloads and identify performance, scalability, and efficiency bottlenecks at rack- and system-scale. • Analyze representative and emerging AI workloads (e.g., LLMs, DLRMs, and future AI models) to derive architecture requirements and trade-offs across compute, memory, networking, and power. • Drive architecture-level design decisions through quantitative modeling, design-space exploration, and performance/power projections. • Perform comparative studies of alternative system architectures, reporting performance and performance-per-watt metrics to guide strategic technology choices. • Collaborate closely with cross-functional teams in hardware architecture, memory, interconnect, and system engineering to align modeling insights with implementation realities. • Communicate architectural insights and recommendations through clear technical presentations and documentation. • Occasional domestic and international travel (