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.)

$75,000 – $150,000 a yearSalary and equity compensation will vary with experience

Extropic is an equal opportunity employer

$20,000 — $55,000/year