Full-Time

ML Compiler Engineer

Staff

Confirmed live in the last 24 hours

d-Matrix

d-Matrix

51-200 employees

AI compute platform for datacenters

Hardware
Enterprise Software
AI & Machine Learning

Senior, Expert

Toronto, ON, Canada

Hybrid position requiring onsite presence in Toronto for 3 days per week.

Category
FinTech Engineering
Software Engineering
Required Skills
Tensorflow
Pytorch
C/C++
Requirements
  • Bachelor's degree in Computer Science with 7+ Yrs of relevant industry experience, MSCS Preferred with 5+ 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.
  • 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.
Responsibilities
  • Help develop the compiler backend - specifically the problem of assigning hardware resources in a spatial architecture to execute low level instructions.
  • Solve algorithmic compiler problems and learn intricate details of the underlining hardware and software architectures.
  • Join a team of experienced compiler developers to ramp up in the compiler infrastructure.
  • Attack the important problem of mapping low level instructions to hardware resources.
  • Work on model partitioning (pipelined, tensor, model and data parallelism), tiling, resource allocation, memory management, scheduling and optimization (for latency, bandwidth and throughput).

d-Matrix focuses on improving the efficiency of AI computing for large datacenter customers. The main product is the digital in-memory compute (DIMC) engine, which combines computing capabilities directly within programmable memory. This design helps reduce power consumption and enhances data processing speed while ensuring accuracy. Unlike many competitors, d-Matrix offers a modular and scalable approach, utilizing low-power chiplets that can be tailored for different applications. The goal is to provide high-performance AI inference solutions that are energy-efficient, catering specifically to the needs of large-scale datacenter operators.

Company Stage

Series B

Total Funding

$149.8M

Headquarters

Santa Clara, California

Founded

2019

Growth & Insights
Headcount

6 month growth

-14%

1 year growth

-3%

2 year growth

235%
Simplify Jobs

Simplify's Take

What believers are saying

  • Securing $110 million in Series B funding positions d-Matrix for rapid growth and technological advancements.
  • Their Jayhawk II silicon aims to solve critical issues in AI inference, such as cost, latency, and throughput, making generative AI more commercially viable.
  • The company's focus on efficient AI inference could attract significant interest from data centers and enterprises looking to deploy large language models.

What critics are saying

  • Competing against industry giants like Nvidia poses a significant challenge in terms of market penetration and customer acquisition.
  • The high dependency on continuous innovation and technological advancements could strain resources and lead to potential setbacks.

What makes d-Matrix unique

  • d-Matrix focuses on developing AI hardware specifically optimized for Transformer models, unlike general-purpose AI chip providers like Nvidia.
  • Their digital in-memory compute (DIMC) architecture with chiplet interconnect is a first-of-its-kind innovation, setting them apart in the AI hardware market.
  • Backed by major investors like Microsoft, d-Matrix has the financial support to challenge established players like Nvidia.

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