Member of Technical Staff - Thermodynamic ML
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.
- 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 algorithms, and benchmarks.
- Publish papers, contribute to open source, and communicate design insights to our hardware team.
- Create production models for domain experts using customer data.
- Experience in scientific Python and at least one deep learning framework (PyTorch, JAX, TensorFlow, Keras)
- Extremely strong foundations in probability and linear algebra
- Familiarity with deep learning theory and literature, including theory of over-parameterization and scaling laws
- Publications in top ML conferences (NeurIPS, ICML, ICLR, CVPR)
- Experience training high-performance models, including familiarity with infrastructure (Slurm, Ray, Weights & Biases)
- Experience deploying models, including familiarity with infrastructure (Ray, AWS, ONNX)
- 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)
- Strong theoretical background in information geometry
- Strong theoretical background in random matrix theory
- Strong grasp of computational Bayesian methods, including MCMC sampling methods and variational inference
Extropic is an equal opportunity employer
Average salary estimate
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If you're ready to take on a challenging and exciting role as a Member of Technical Staff - Thermodynamic ML at Extropic in Boston, then we’d love to hear from you! At Extropic, we are revolutionizing the way probabilistic inference is processed by our cutting-edge hardware. Our dynamic ML team is seeking seasoned researchers and engineers to dive deep into the thermodynamic principles behind training models. As a senior member of our team, you will lead your own research direction, quickly becoming an expert in our unique stack, which covers hardware, software, and the complex interplay of physics and mathematics. Your responsibilities will include collaborating with our talented researchers and engineers to develop and test new probabilistic models like energy-based and diffusion models. You'll be scaling up our experimental infrastructure and implementing innovative training algorithms and architectural benchmarks. A key part of your role will also involve publishing your findings and sharing insights with our hardware teams, as well as designing production models that are tailored to meet the needs of domain experts using real customer data. If you have the experience in scientific programming, deep learning frameworks, and a solid foundation in probability and linear algebra, then you already have a strong start to joining our team. We offer a competitive salary, based on your experience, along with equity compensation. So, are you ready to push the boundaries of ML with us?
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