Full-Time

Machine Learning Computer Architect

Staff, Workload Analysis

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

Entry, Junior, Mid

Santa Clara, CA, USA

Requires onsite presence in Santa Clara, CA for three days per week.

Category
Applied Machine Learning
AI Research
AI & Machine Learning
Required Skills
Python
C/C++
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 analytical performance models, architecture simulators for performance analysis, or hacking existing ones such as cycle-level simulators (gem5, GPGPU-Sim 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 analyze the latest ML workloads (multi-modal LLMs, CoT reasoning models, video/audio-generation).
  • You will contribute Hardware and Software 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, and collaborate with different partner teams including Product, Hardware design, Compiler, Inference Server, Kernels.
  • Your day-to-day work will include (1) analyzing the properties of emerging machine learning algorithms and workloads and identifying functional, performance implications (2) Creating analytical models to project performance on current and future generations of d-matrix hardware (3) proposing new HW/SW features to enable or accelerate these algorithms.

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

219%
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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