Principal Software Engineer, Performance Analysis and Modeling

At d-Matrix, we are focused on unleashing the potential of generative AI to power the transformation of technology. We are at the forefront of software and hardware innovation, pushing the boundaries of what is possible. Our culture is one of respect and collaboration.

We value humility and believe in direct communication. Our team is inclusive, and our differing perspectives allow for better solutions. We are seeking individuals passionate about tackling challenges and are driven by execution.  Ready to come find your playground? Together, we can help shape the endless possibilities of AI. 

Working onsite at our Santa Clara, CA, headquarters 3 days per week hybrid. Will consider remote in the United States. Will consider remote in the United States.

The role: Principal Software Engineer, Performance Analysis and Modeling

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

Equal Opportunity Employment Policy

d-Matrix is proud to be an equal opportunity workplace and affirmative action employer. We’re committed to fostering an inclusive environment where everyone feels welcomed and empowered to do their best work. We hire the best talent for our teams, regardless of race, religion, color, age, disability, sex, gender identity, sexual orientation, ancestry, genetic information, marital status, national origin, political affiliation, or veteran status. Our focus is on hiring teammates with humble expertise, kindness, dedication and a willingness to embrace challenges and learn together every day.

d-Matrix does not accept resumes or candidate submissions from external agencies. We appreciate the interest and effort of recruitment firms, but we kindly request that individual interested in opportunities with d-Matrix apply directly through our official channels. This approach allows us to streamline our hiring processes and maintain a consistent and fair evaluation of al applicants. Thank you for your understanding and cooperation.