Lead Performance Modeling Architect, Cpu Fabric and Llc

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

Lead Performance Modeling Architect for CPU Fabric and LLC at NVIDIA, focusing on next-generation cache hierarchies and I/O coherent interconnects for Automotive and Data Center platforms. The role involves guiding a team, defining modeling infrastructure vision, and driving advanced modeling methodologies.

What you'd actually do

  1. Defining the long-term vision for our modeling infrastructure, choosing between cycle-accurate, analytical, and stochastic modeling approaches to meet project achievements.
  2. Leading a team of modeling engineers, offering in-depth technical mentorship, conducting code/architecture reviews, and encouraging a culture of rigorous data-driven decision-making.
  3. Act as the primary liaison between Architecture, RTL Build, and Software teams to resolve complex performance bottlenecks and trade-offs.
  4. Drive the adoption of advanced modeling methodologies (e.g., hybrid emulation/simulation, AI-based performance optimization) to accelerate the build cycle.
  5. Allocate simulation workloads and engineering efforts across several simultaneous projects in the automotive and data center sectors.

Skills

Required

  • Master’s or Ph.D. in Computer Engineering or related field (or equivalent experience)
  • 8+ years of experience in high-performance silicon architecture
  • Extensive experience managing technical teams or complex projects in performance modeling or computer architecture
  • Proficiency in cache coherency protocols (e.g., AMBA CHI, MESI)
  • Proficiency in memory sub-systems
  • Proficiency in high-speed interconnect fabric build
  • Significant experience building and architecting large-scale simulators in C++ or SystemC
  • Experience with modularity and simulation speed in simulators
  • Track record of using statistical analysis to validate model accuracy against RTL or silicon
  • Ability to explain complex performance 'cliffs' to executive collaborators

Nice to have

  • Full-Stack Performance Experience (whiteboard sketch to post-silicon tuning)
  • Standardization Influence (industry bodies, published architectural research)
  • Scalability Expertise (low-latency, safety-critical clusters, high-bandwidth mesh networks)
  • Critical Thinking (explaining architectural decisions' impact on TCO or safety margins)

What the JD emphasized

  • performance modeling
  • computer architecture
  • large-scale simulators
  • model accuracy
  • performance bottlenecks
  • performance-per-watt
  • AI-based performance optimization