Principal Architect, Performance Analysis and Modeling

About the role

d-Matrix is looking for a computer engineer to help analyze and model performance across the hardware/software boundary of our AI inference accelerators. This role focuses on emerging hardware technologies (DIMC, D2D, 3D-DRAM) and emerging workloads (generative inference, multi-modal LLMs) — building the analytical models and simulation tools that let the architecture team project performance on current and future d-Matrix silicon. You'll work closely with Hardware Design, Compiler, Inference Server, and Kernels teams, translating workload analysis into concrete modeling inputs and surfacing HW/SW improvement opportunities. This is a hands-on technical contributor role within the broader architecture organization.

What You Will Do

• Analyze emerging ML workloads, multi-modal LLMs, CoT reasoning models, and video/audio generation to identify performance-relevant properties
• Build and maintain analytical performance models that project behavior on current and future d-Matrix hardware generations
• Develop and extend architecture simulators to support performance analysis of proposed HW/SW features
• Partner with hardware design, compiler, inference server, kernel, and product teams to validate modeling assumptions and surface downstream implications
• Track relevant ML architecture and algorithms research and incorporate findings into modeling work
• Propose targeted HW/SW feature improvements based on modeling results and workload analysis
• Document modeling methodology and findings for reuse across the architecture team

What You Will Bring

• BSEE with 6+ years of industry experience, or MSEE with 4+ years of industry experience
• Working knowledge of computer architecture, HW/SW co-design, performance modeling, and ML fundamentals (particularly DNNs)
• Programming fluency in C/C++ or Python
• Experience building or working with analytical performance models or architecture simulators
• Self-motivated and collaborative, comfortable working across hardware and software teams

Preferred Qualifications

• Experience optimizing AI/ML workloads on accelerator technologies
• Research and investigation in AI/ML architecture/microarchitecture