ashby
Machine Learning Researcher - Springtail
Astera Institute
Emeryville, USA
Full-time
Mid
Hybrid
USD 150000-300000 yearly / year
Discovered 1 weeks ago
PyTorchJAXCUDATriton
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Role Overview
Datasets in many areas, science in particular, are often small, heterogeneous, and expensive. Human scientists can take these datasets and generate models to describe them, but this process of model induction is labor-intensive and error-prone. Machine learning is a general and scalable solution, but it is not uniformly sample efficient.
The Astera Institute is seeking a Machine Learning Researcher to help surmount this barrier with new architectures for data-efficient and general model induction. This includes bootstrapped program synthesis, along with components for a system that synthesizes its own learning algorithms - a machine learning strange loop. This is a full time position that reports to Timothy Hanson.
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PyTorchJAXCUDATriton
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Position Summary
Datasets in many areas, science in particular, are often small, heterogeneous, and expensive. Human scientists can take these datasets and generate models to describe them, but this process of model induction is labor-intensive and error-prone. Machine learning is a general and scalable solution, but it is not uniformly sample efficient.
The Astera Institute is seeking a Machine Learning Researcher to help surmount this barrier with new architectures for data-efficient and general model induction. This includes bootstrapped program synthesis, along with components for a system that synthesizes its own learning algorithms - a machine learning strange loop. This is a full time position that reports to Timothy Hanson.
Responsibilities:
- Hypothesize, test, and refine means of improving generalization performance of common architectural elements, including different forms of attention. This includes devising controlled datasets to elucidate e.g. learning order & learned representations.
- Think both mathematically and empirically about problems of runtime inference in gradient-trained networks, with an eye to the extensive literature on statistical learning and an open mind to the many forms of constrained optimization.
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