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Ayo Semiconductor

Photonic processors for AI workloads

AI Lead

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeBoston, MA, USA

About the Job

Requirements

  • A PhD in machine learning, representation learning, theory of computation, or a related field, or equivalent industry experience working on foundation models at scale.
  • Experience training models at scale, including distributed training across many graphics processing units, large datasets, and substantial compute.
  • Experience building and training neural networks from scratch.
  • Deep knowledge of the structure and internal operation of neural networks, including how and why they behave.
  • Interest in applying deep artificial intelligence expertise to novel hardware environments and willingness to learn about photonics or silicon.
  • Fluent knowledge of Python.
  • Fluency in TensorFlow, JAX, or another industry-standard machine learning software library.

Responsibilities

  • Deploy and run trained models on prototype hardware and digital twins, producing working demonstrations on the chips.
  • Develop and adapt algorithms to train models on novel processing environments, including prototype hardware.
  • Work with hardware engineers to define and refine processor architecture based on insights from model training and experimentation.
  • Apply deep curiosity about artificial intelligence systems to understanding how they run on new hardware.
  • Support go-to-market strategy development.

Desired Qualifications

  • Experience with interpretability or explainability work.
  • Fluency in PyTorch is preferred.

About the Company

Ayo Semiconductor

Photonic processors for AI workloads

Ayo Semiconductor designs and prototype photonic processors that use light to perform AI computations. Instead of relying only on electronic transistors, their integrated photonic circuits process information with photons, aiming to run AI workloads faster and with less energy than today’s GPU-based systems. The company’s approach centers on building integrated photonic circuits and hardware for AI compute, offering a different path from traditional electronics to increase throughput and reduce power use. The goal is to enable significantly faster and more energy-efficient AI processing by bringing photonics into practical AI hardware.

Company size
11-50
Company stage
Not listed
Total funding
Not listed
Headquarters
Boston, MA
Founded
Not listed

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