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Extropic

Energy-efficient probabilistic silicon AI accelerator

Thermo Machine Learning Resident

AI ResearchConfirmed live in the last 24 hours
Full-timeSan Francisco, CA, USA$75k - $200k /yrIn-person

About the Job

Requirements

  • Experience in scientific Python with JAX or a similar deep learning framework such as PyTorch, TensorFlow, or Keras.
  • Strong foundations in probability and linear algebra.
  • Projects or papers demonstrating hands-on experience in applied machine learning and data science.

Responsibilities

  • Collaborate with senior researchers to derive the theory of new probabilistic models and their learning rules, including energy-based models and diffusion models.
  • Scale up experimentation infrastructure and optimize over the design space of models.
  • Implement, visualize, and evaluate new architectures, training algorithms, and benchmarks.
  • Publish papers, contribute to open source, and communicate design insights to the hardware team.

Desired Qualifications

  • Familiarity with deep learning theory and literature, including the theory of over-parameterization and scaling laws.
  • Experience training energy-based models or diffusion models.
  • Experience with graph neural networks or graph message passing algorithms.
  • Experience with infrastructure for deep learning experimentation and training, including Slurm, Ray, Kubernetes, and Weights & Biases.
  • Strong theoretical background in information geometry.
  • Strong grasp of computational Bayesian methods, including MCMC sampling methods and variational inference.
  • Publications in top machine learning conferences such as NeurIPS, ICML, ICLR, and CVPR.

About the Company

Extropic

Energy-efficient probabilistic silicon AI accelerator

Extropic builds a silicon-based probabilistic chip and a full hardware platform to accelerate generative AI. It uses thermodynamic accelerated computing, turning natural matter fluctuations into computation to perform probabilistic tasks like Monte Carlo simulations. This approach distinguishes Extropic from traditional chips by focusing on physics-based, energy-efficient probabilistic processing and providing a complete hardware stack rather than just a component. The goal is to reshape AI hardware so large models can run more efficiently, with lower energy use and fewer bottlenecks.

Company size
11-50
Company stage
Seed
Total funding
$14.1M
Headquarters
Waltham, MA
Founded
2022

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