Role Overview
We are looking for a Performance Modeling Architect who will develop the modeling and simulation frameworks used to guide architectural decisions across our platform. In this role, you will build models that capture the interaction between AI workloads, software stacks, and hardware architectures, enabling rigorous evaluation of system-level tradeoffs. Your models will represent not only the SoC architecture and its components, but also key elements of the software stack and system behavior that ultimately determine real-world performance. These frameworks will enable hardware–software co-design, allowing us to explore design alternatives and quantify their impact on
performance, latency, scalability, and energy efficiency. Your work will directly influence the architecture of next-generation AI compute platforms.
Responsibilities
● Develop system-level performance models for AI workloads and compute architectures.
● Build simulation frameworks that capture interactions between AI models, software runtimes, and hardware systems.
● Model key components of SoC architecture including compute units, memory hierarchies, and interconnects.
● Incorporate relevant software and runtime behavior into modeling frameworks to reflect realistic system execution.
● Analyze system bottlenecks and evaluate architectural tradeoffs across the platform.
● Work closely with hardware architects to guide microarchitectural and system-level design decisions.
● Collaborate with software and ML teams to incorporate realistic workloads into modeling frameworks.
● Use modeling and simulation to evaluate performance, latency, throughput, and energy efficiency across target applications.
Required Qualifications
● Strong background in computer architecture, system architecture, or performance modeling.
● Experience building simulation or analytical models of complex hardware systems.
● Strong programming skills (Python, C++, or similar) for modeling and simulation frameworks.
● Understanding of modern compute architectures such as CPUs, GPUs, or AI accelerators.
● Ability to analyze complex systems and identify performance bottlenecks.
● Strong quantitative reasoning and ability to translate models into architectural
insights.
Preferred Qualifications
● Experience modeling AI workloads or machine learning systems.
● Familiarity with architectural simulators or performance modeling tools.
● Experience with memory systems, interconnect architectures, or accelerator design.
● Exposure to hardware–software co-design methodologies.
● Experience modeling performance across multiple layers of the stack, including software runtimes and hardware platforms.
