Thermo ML Resident — Extropic

Extropic, a Boston-based hardware startup, is hiring junior ML scientists for its Thermo ML Resident program, offering a salary of $75,000–$200,000 per year for on-site work. Residents will collaborate with senior researchers to develop theory and training methods for probabilistic models in the thermodynamic computing paradigm, with responsibilities including publishing papers and contributing to open source. The role requires experience in scientific Python, JAX or similar frameworks, and strong foundations in probability and linear algebra.

Thermo ML Resident - Salary - $75k–200k/yr - Location - Boston - Work type - On-site - Posted - today Apply on company site opens in new tab https://jobs.ashbyhq.com/extropic/f9d4811c-3a4c-4a6a-a739-8719ff9c6bab/application 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.