Thermo ML Resident

OverviewExtropic 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.nResponsibilitiesCollaborate with senior researchers to derive the theory of new probabilistic models and their learning rules, including energy-based models and diffusion modelsScale up experimentation infrastructure and optimize over the design space of modelsImplement, visualize, and evaluate new architectures, training algorithms, and benchmarksPublish papers, contribute to open source, and communicate design insights to our hardware teamRequired QualificationsExperience in scientific PythonExperience with JAX or similar deep learning framework (PyTorch, TensorFlow, or Keras)Strong foundations in probability and linear algebraProjects or papers demonstrating hands-on experience in applied machine learning and data scienceFamiliarity with deep learning theory and literature, including theory of over-parameterization and scaling lawsPreferred QualificationsExperience training energy-based models (EBMs) or diffusion modelsExperience 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 geometryStrong grasp of computational Bayesian methods, including MCMC sampling methods and variational inferencePublications in top ML conferences (NeurIPS, ICML, ICLR, CVPR, etc.)n$75,000 - $150,000 a yearSalary and equity compensation will vary with experiencenExtropic is an equal opportunity employerPlease mention the word **IRREPROACHABLE** and tag RMzguNjguMTM0LjE5NA== when applying to show you read the job post completely (#RMzguNjguMTM0LjE5NA==). This is a beta feature to avoid spam applicants. Companies can search these words to find applicants that read this and see they're human.

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