Extropic

Research Scientist - Machine Learning

San FranciscohybridUSD 180,000–250,000
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Skills

scientific Pythondeep learning framework (PyTorch, JAX, TensorFlow, Keras)probability and linear algebradeep learning theory and literaturetheory of over-parameterization and scaling lawstraining high-performance modelsinfrastructure (Slurm, Ray, Weights & Biases)deploying modelsinfrastructure (Ray, AWS, ONNX)

About the role

Research Scientist - Machine Learning San Francisco ML Team / Full-time / Hybrid Overview Extropic’s hardware massively accelerates certain kinds of probabilistic inference. Our ML team works on the science of training models in the thermodynamic paradigm, and we are looking for senior research and engineering talent to derive probabilistic ML theory, empirically demonstrate its scaling properties, and deploy performant models. Senior hires will be leading their own research direction and are therefore expected to quickly become experts across our abstraction stack, including the hardware, software, physics, and math. Responsibilities - Collaborate with senior researchers, residents, engineers, and physicists 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 a

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