Posted on:September 7, 2026

Thermo ML Resident at Extropic

Extropic is hiring a Thermo ML Resident in Boston, MA, US +1 more. On-site. Pay: USD 75k-200k/yr.

About Extropic

Extropic builds thermodynamic computing hardware that is more energy-efficient than GPUs. Its hardware, including thermodynamic sampling units (TSUs), is inherently probabilistic and aimed at probabilistic AI workloads. The company also provides open-source software frameworks and an API for programming and simulating workloads on its hardware.

Thermo ML Resident job description

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.

Apply now

Applications go straight to Extropic. We never sit between you and the employer.

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