Thermo ML Resident

Overview

Extropic is looking for junior ML scientists to join our residency program on either a part-time or full-time basis. 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
• Experience 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
• Familiarity with deep learning theory and literature, including theory of over-parameterization and scaling laws

Preferred Qualifications

• 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.