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# Machine Learning Researcher

- **Company:** Astera Institute
- **Location:** Emeryville, CA, United States
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Artificial Intelligence, Nvidia CUDA, Data Files, Machine Learning, Open Source Technology, Extensible Markup Language (XML), Pytorch
- **Published:** July 31, 2026
- **Apply:** https://www.careerbuilder.com/job-details/machine-learning-researcher-springtail-ca--07d99936-966c-45c5-9656-7f93b35fa178

## About the Role

* Masters or equivalent in machine learning, mathematics, or equivalent fields (strong candidates from neuroscience are encouraged to apply).
 * Fluency with Pytorch, and familiarity with JAX, CUDA, and/or Triton+ their open-source ecosystems.
 * Demonstrated ability do fundamental research.
 * Demonstrated ability to work in teams., Algorithms, Architectural Services, Artificial Intelligence (AI), CUDA (Compute Unified Device Architecture), Data Sets, Ecosystems, JAX (Java API for XML), Leadership, Machine Learning, Mathematics, Neuroscience, Nonprofit, Open Source, Performance Management, Process Modeling, Research Skills, Startup, Statistical Learning Theory

## Description

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.
 * Contribute to a well-documented and well-instrumented code base that is performant where necessary yet expeditious where experimental throughput demands.


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