Hybrid, working onsite at our Santa Clara, Ca Headquarters 3 days per week.
d-Matrix is looking for a AI System Solutions Architect to develop world-class products around d-Matrix inference accelerators. In this role you will be engaged with key customers and internal architects, and other key internal and external stakeholders to drive overall system solutions. This requires technically analyzing, defining outside-in usage cases, and use broad spectrum of technologies to drive a AI server system solution spanning silicon, platform HW/SW, and usages to deliver the best customer experiences with d-Matrix inference accelerators.
- Design, develop, and deploy scalable GenAI inference solutions with d-Matrix accelerators
- Work closely with team members across architecture, engineering, product management and business developments to optimize the d-Matrix system solutions for best performance & power balance, feature set and overall system cost.
- Work closely with Datacenter, OEM and ODM customers at early stage of product concept and planning phase, to enable the system design with partners and industrial ecosystem.
- Influence and shape the future generations of products and solutions by contributing to the system architecture and technology through the early engagement cycle with customers and industrial partners.
- Stay abreast of the latest advancements in GenAI hardware and software technologies and assess their suitability for integration into d-Matrix GenAI inference solutions.
- Establish credibility with both engineering and leadership counterparts at top technology companies, communicate technical results and positions clearly and accurately, and drive alignment on solutions.
- 15 + Years of Industry Experience and Engineering degree in Electrical Engineering, Computer Engineering, or Computer Science with extensive experience.
- 5+ years of AI Server System experience by working on multiple projects from architecture, development, design including memory, I/O, power delivery, power management, boot process, FW and BMC/hardware management through bring-up and validation and supported through the release to production.
- 5+ years of experience in a customer-facing role interfacing with OEMs, ODMs and CSPs.
- Detailed understanding of server industry standard busses, such as DDR, PCIe, CXL and other high-speed IO protocol is required.
- Ability to work seamlessly across engineering disciplines and geographies to deliver excellent results.
- Deep understanding of datacenter AI infrastructure requirements and challenge
Preferred:
- Hands-on understanding of AI/ML infrastructure and hardware accelerators
- Experience with leading AI/ML frameworks such as PyTorch, TensorFlow, ONNX, etc. and container orchestration platforms such as Kubernetes
- Outstanding communication and presentation skills
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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.
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.
What We Do
d-Matrix is building a new way of doing datacenter AI inferencing using in-memory computing (IMC) techniques with chiplet level scale-out interconnects. Founded in 2019, d-Matrix has attacked the physics of memory-compute integration using innovative circuit techniques, ML tools, software and algorithms; solving the memory-compute integration problem, which is the final frontier in AI compute efficiency.