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Thermo ML Resident

Extropic
Posted a day ago, valid for 22 days
Location

California, PA, US

Salary

$75,000 - $200,000 per year

Contract type

Full Time

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Sonic Summary

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  • Extropic is seeking junior ML scientists for a flexible residency program that can be part-time or full-time, lasting a minimum of 3 months.
  • Residents will collaborate with senior researchers on the development of new probabilistic models and their learning rules, utilizing advanced hardware for model training.
  • Candidates should have experience in scientific Python with frameworks like JAX, PyTorch, or TensorFlow, along with a strong foundation in probability and linear algebra.
  • Preferred qualifications include familiarity with deep learning theory, experience with energy-based or diffusion models, and a strong theoretical background in information geometry.
  • The salary for this position is competitive, and candidates are expected to have relevant experience in applied machine learning and data science.

Overview

Extropic is looking for junior ML scientists to join our residency program on either a part-time or full-time basis. This is a flexible program that can be similar to an internship (minimum 3 months) but we give our residents far more autonomy than most internships.

Our hardware massively accelerates certain kinds of probabilistic inference, and residents will help pioneer the science of training models in the thermodynamic paradigm.

 
 

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 our hardware team

     

Required Qualifications

  • Experience in scientific Python with JAX or similar deep learning framework (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

     

Preferred Qualifications

  • Familiarity with deep learning theory and literature, including theory of over-parameterization and scaling laws

  • Experience training energy-based models (EBMs) or diffusion models

  • Experience with graph neural networks (GNNs) or graph message passing algorithms

  • Experience with infrastructure for deep learning experimentation and training (Slurm, Ray, Kubernetes, Weights & Biases, etc.)

  • Strong theoretical background in information geometry

  • Strong grasp of computational Bayesian methods, including MCMC sampling methods and variational inference

  • Publications in top ML conferences (NeurIPS, ICML, ICLR, CVPR, etc.)

Extropic is an equal opportunity employer

This position will require access to information subject to control under U.S. export control laws and regulations, including the Export Administration Regulations (“EAR”). Please note that any offer for employment will be conditioned on authorization to receive controlled items.




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