Thermo ML Resident - San Francisco

Originally posted 8 September 2026 by the employer.

About the role

Extropic is hiring a Thermo ML Resident in San Francisco to conduct research on probabilistic inference models within a thermodynamic computing framework.

What you'll do

- 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.

What you'll need

- 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.

Nice to have

- 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 ML conferences such as NeurIPS, ICML, ICLR, or CVPR.