System Performance & AI Architect

The Mission

As 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 Responsibilities

• System-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.
  
  

Requirements:

Technical Qualifications

• Education: PhD in Electrical Engineering, Computer Science, or a related field with a focus on Computer Architecture or High-Performance Systems.
• Experience: 10 years of experience in performance modeling and system architecture, with a proven track record at major semiconductor or hyper-scale AI organizations.
• Expertise in Data-Centric Computing: Deep understanding of Instruction Set Architectures (ISA), Cache Coherence, and Memory Consistency.
• Expertise in modeling Interconnect Topologies and flow control for distributed AI training and inference.
• Advanced proficiency in Modern C and Python for building sophisticated system-level simulators.
• Profiling Mastery: Hands-on experience with performance analysis tools (e.g., NSight, ROCm, VTune) and developing custom trace-injection tools to correlate silicon behavior with simulation models.
• Workload Mastery: Demonstrated ability to characterize and optimize for irregular data-flow patterns common in GNNs, LLMs, and Recommendation Systems.
  
  

Professional Leadership

• Strategic Influence: Experience presenting data-driven architectural recommendations to executive leadership and strategic partners based on a blend of simulation and empirical data.
• Customer Engagement: Proven ability to translate customer-facing performance requirements into actionable hardware specifications.
• Technical Mentorship: A history of elevating the technical bar for engineering teams and championing modern, automated engineering workflows.