Research Scientist - Machine Learning - San Francisco, United States

Originally posted 30 June 2026 by the employer.

About the role

Extropic is hiring a Research Scientist - Machine Learning in San Francisco to derive probabilistic ML theory, demonstrate empirical scaling properties, and deploy models on thermodynamic hardware.

What you'll do

- Collaborate with researchers, residents, engineers, and physicists to derive the theory of new probabilistic models and 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. - Create production models for domain experts using customer data.

What you'll need

- Experience in scientific Python and at least one deep learning framework (PyTorch, JAX, TensorFlow, Keras). - Strong foundations in probability and linear algebra. - Familiarity with deep learning theory and literature, including theory of over-parameterization and scaling laws. - Publications in ML conferences (NeurIPS, ICML, ICLR, CVPR). - Experience training high-performance models and familiarity with infrastructure including Slurm, Ray, and Weights & Biases. - Experience deploying models, including familiarity with infrastructure such as Ray, AWS, and ONNX.

Nice to have

- Experience designing probabilistic graphical models (PGM). - Experience training energy-based models (EBMs) or diffusion models. - Experience with numerical methods in diffeq solvers. - Experience with message passing or training graph neural networks (GNNs). - Theoretical background in information geometry. - Theoretical background in random matrix theory. - Grasp of computational Bayesian methods, including MCMC sampling methods and variational inference.