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Full-Time

Machine Learning Computer Architect

Confirmed live in the last 24 hours

d-Matrix

d-Matrix

51-200 employees

AI compute platform using in-memory computing

Data & Analytics
Hardware
AI & Machine Learning

Mid

Santa Clara, CA, USA

Category
Applied Machine Learning
AI Research
AI & Machine Learning
Required Skills
Python
Data Structures & Algorithms
Requirements
  • MS, PHD, MSEE with 3+ years of experience or PhD with 0-1 year of applicable experience.
  • Solid grasp through academic or industry experience in multiple of the relevant areas – computer architecture, hardware software codesign, performance modeling, ML fundamentals (particularly DNNs).
  • Programming fluency in C/C++ or Python.
  • Experience with developing architecture simulators for performance analysis, or hacking existing ones such as cycle-level simulators (gem5, GPGPU-Sim etc.) or analytical models (Timeloop, Masetro etc.).
  • Research background with publication record in top-tier architecture, or machine learning venues is a huge plus (such as ISCA, MICRO, ASPLOS, HPCA, DAC, MLSys etc.).
  • Self-motivated team player with strong sense of collaboration and initiative.
Responsibilities
  • As a member of the architecture team, you will contribute to features that power the next generation of inference accelerators in datacenters.
  • This role requires to keep up the latest research in ML Architecture and Algorithms space, and collaborate with different partner teams including hardware design, compiler.
  • Your day-to-day work will include (1) analyzing the properties of emerging machine learning algorithms and identifying functional, performance implications (2) proposing new features to enable or accelerate these algorithms, (3) studying the benefits of proposed features with performance models (analytical, cycle-level).

d-Matrix is developing a unique AI compute platform using in-memory computing (IMC) techniques with chiplet level scale-out interconnects, revolutionizing datacenter AI inferencing. Their innovative circuit techniques, ML tools, software, and algorithms have successfully addressed the memory-compute integration problem, enhancing AI compute efficiency.

Company Stage

Series B

Total Funding

$161.5M

Headquarters

Santa Clara, California

Founded

2019

Growth & Insights
Headcount

6 month growth

-14%

1 year growth

85%

2 year growth

223%
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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.