d
d-Matrix
Principal Architect
Skills
PythonMachine LearningAI
2 months ago
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Important information
Salary:
$175,000 – $285,000 / year
Location:
San Francisco, CA, USA
Work mode:
Onsite
Published on:
4 September 2026
What they need
Who you are
- 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
- Experience optimizing AI/ML workloads on accelerator technologies
- Research and investigation in AI/ML architecture/microarchitecture
What the job involves
- 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
- 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
Benefits
- Equity
- Health care
- Flexible time-off
- Paid paternity leave
- 401k retirement plan
- Remote & hybrid working model
- Work with world-leading engineers and researchers
- Peer coaching & development
- Team-building activities
- Happy hours
- Free food and snacks
- On-site fitness center
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