Senior Runtime Systems Engineer

Reposted 9 Days Ago
Be an Early Applicant
Santa Clara, CA
In-Office
70-150 Annually
Senior level
Artificial Intelligence • Machine Learning • Software
The Role
Responsible for architecture, development, and validation of firmware/software for AI inference on multiprocessor SoCs, ensuring runtime performance. Collaborate with hardware teams and deliver quality code.
Summary Generated by Built In

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. 

Location:

Hybrid, working onsite at our Santa Clara, CA headquarters 3 days per week.

The role: Senior Runtime Systems Engineer

What You Will Do:

d-Matrix is developing an AI compute platform focusing on in-memory compute for AI inference in datacenters.

This position is for Runtime Software Engineering working on the architecture, development, and validation of the functionality and efficiency of firmware/software that is executed on multiprocessor system-on-a-chip, low-level drivers, and systems software that hosts this SoC.

In this role, you will be largely responsible for all aspects of runtime performance of the silicon product. You will architect, document, and develop the runtime firmware that executes in various on-chip multi-core CPU subsystems.

You will be responsible for determining the delivery schedule and ensuring the software meets d-Matrix coding and methodology guidelines. You will collaborate with the hardware team, hardware verification team, and other members of the software team

 

Key Responsibilities:

  • Design, implement and develop systems software for AI inference infrastructure

  • Identify, analyze, architect, develop and debug systems software

  • Experience in distributed and scale-out application

  • Deliver quality code; debug complex problems

Qualification:

  • Bachelor’s in computer engineering or electrical engineering with a minimum of 5 years of industry experience in embedded software development

  • Worked with containers and VMs

  • Experience with Linux system level programming

  • Strong knowledge in Linux device driver

  • Proficiency in C/C++

  • Experience working with python is desired

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.

Top Skills

C,C++,Python,Linux,Embedded Software,Containers,Vms
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The Company
HQ: Santa Clara, CA
102 Employees

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.

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