The MissionAs a ;System Architect, you will be responsible for the end-to-end performance simulation of our next-generation AI and Graph-computing platforms. You will perform ; the quantitative analysis and architectural pathfinding that defines how our systems handle the world’s most complex data-centric workloads, from Trillion-parameter LLMs and Mixture-of-Experts (MoE) to large-scale, irregular Graph Neural Networks (GNNs), and other workloads.Key ResponsibilitiesSystem-Level Performance Projection: ; develop and execute high-fidelity, system-level performance models that simulate the interaction between compute clusters, Network-on-Chip (NoC), and advanced memory hierarchies (HBM4, CXL).Empirical Profiling & Characterization: Drive deep-dive performance profiling of existing hardware architectures (GPUs, NPUs, and SoCs). Use hardware counters, trace-based analysis, and telemetry to identify real-world bottlenecks in current silicon that inform future architectural iterations.Workload-Architecture Co-Design: Profile frontier AI models and graph analytics to identify deep-system bottlenecks. Translate high-level algorithmic behaviors (e.g., KV cache growth, sparse matrix traversals) into hardware architectural requirements.Memory Subsystem Innovation: Define the strategy for managing the "Memory Wall," optimizing for bandwidth, latency, and power across complex hierarchies and disaggregated memory pools.Architectural Pathfinding: Evaluate and influence the adoption of emerging system technologies. Conduct trade-off analyses that determine the multi-year roadmap for system topology and scalability.AI-Augmented Engineering: Champion an "AI-first" approach to architecture, utilizing machine learning and automation to accelerate simulation throughput and explore massive design spaces.Cross-Functional Technical Leadership: Serve as a primary bridge between Software/Compiler teams and Hardware Implementation, ensuring architectural specifications meet real-world production constraints.