Staff Engineer, Developer and Qualification Tools

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Santa Clara, CA
In-Office
Artificial Intelligence • Machine Learning • Software
The Role

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:

Santa Clara, CA, headquarters, but open to other locations within the US/Canada.

The role: Engineer, Developer, and Qualification Tools Staff

What You Will Do:

The role requires you to be a part of the team that helps productize the SW tools for our AI compute engine. As part of the software team, you will be responsible for the development, enhancement, and maintenance of the tool suite for our ML accelerator systems in a data center environment. The tool suite consists of profilers, debuggers, board management tools such as validation suites, and monitoring and logging tools. The tools expose C/C++, Python, and REST API interfaces to build frontends upon. The user frontends include CLIs, web interfaces, and other visualization tools.

Minimum Qualifications:

  • Computer Science, Engineering, Math, Physics, or related degree

  • 5+ years of industry experience in design of developer and deployment tools

  • Proficient in C/C++, Python, CLI development,

  • Proficient in the design of developer tools, including profiler and debuggers (such as gprof and gdb) for general-purpose processors or accelerators (CPUs, GPUs, FPGAs, and DSP processors).

  • Experience with orchestration tools used for data center deployment of ML workloads such as containers, K8s

  • Proficient in design of deployment tools such as card validation suites, management suites, and logging and analysis software (such Prometheus and Graphana)

  • Experience with ML graph compilation and runtime

  • Experience with designing tools for hardware vendors

Preferred Experience:

  • Prior startup, small team, or incubation experience

  • Work experience at an AI compute/subsystem company

  • Research background with publication record in Computer systems conferences

  • Experience with MLOps and the ML workload deployment and development flow

  • Willing to keep oneself up-to-date with the latest trends and research in the ML community and understand how the trends affect d-Matrix requirements and approach

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.

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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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