ML Compiler Engineer - Backend, Senior

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Toronto, ON
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:

Hybrid, working onsite at our Toronto, Ontario, Canada headquarters 3 days per week.

The role: ML Compiler Engineer - Backend, Senior

What you will do:

The d-Matrix compiler team is looking for exceptional candidates to help develop the compiler backend - specifically the problem of assigning hardware resources in a spatial architecture to execute low level instructions. The successful candidate will be motivated, capable of solving algorithmic compiler problems and interested in learning intricate details of the underlining hardware and software architectures. The successful candidate will join a team of experienced compiler developers, which will be guiding the candidate for a quick ramp up in the compiler infrastructure, in order to attack the important problem of mapping low level instructions to hardware resources. We have opportunities specifically in the following areas: 

  • Model partitioning (pipelined, tensor, model and data parallelism), tiling, resource allocation, memory management, scheduling and optimization (for latency, bandwidth and throughput).

What you will bring:

Minimum:

  • Bachelor's degree in Computer Science with 12+ Yrs of relevant industry experience, MSCS Preferred with 8+ yrs of relevant industry experience.

  • Ability to deliver production quality code in modern C++. 

  • Experience in modern compiler infrastructures, for example: LLVM, MLIR. 

  • Experience in machine learning frameworks and interfaces, for example: ONNX, TensorFlow and PyTorch.

  • Experience in production compiler development. 

Preferred:

  • Algorithm design ability, from high level conceptual design to actual implementation.

  • Experience with relevant Open Source ML projects like Torch-MLIR, ONNX-MLIR, Caffe, TVM.

  • Passionate about thriving in a fast-paced and dynamic startup culture.

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
On-site Workplace

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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