Machine Learning Researcher at Astera Institute focusing on data-efficient and general model induction. Collaborating on innovative architectures with a focus on performance and throughput.
Responsibilities
Hypothesize, test, and refine means of improving generalization performance of common architectural elements
Think both mathematically and empirically about problems of runtime inference in gradient-trained networks
Contribute to a well-documented and well-instrumented code base that is performant where necessary yet expeditious where experimental throughput demands
Requirements
Masters or equivalent in machine learning, mathematics, or equivalent fields
Fluency with Pytorch
Familiarity with JAX, CUDA, and/or Triton + their open-source ecosystems
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