Machine Learning Researcher - Springtail at Astera Institute

Emeryville, California, United States -

Full Time

Start Date

Immediate

Expiry Date

28 Jun, 26

Salary

300000.0

Posted On

30 Mar, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Pytorch, JAX, CUDA, Triton, Program Synthesis, Model Induction, Generalization Performance, Attention Mechanisms, Runtime Inference, Gradient-Trained Networks, Statistical Learning, Constrained Optimization, Code Base Contribution, Empirical Testing, Mathematical Thinking

Industry

Research Services

Description

Astera Institute Machine Learning Researcher - Springtail 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. Contribute to a well-documented and well-instrumented code base that is performant where necessary yet expeditious where experimental throughput demands. Qualifications and Experience 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. About Astera: Astera Institute is a new kind of research organization that operates outside the constraints of markets or academia, and is itself an experiment in how research organizations can move faster, take smarter risks, and create lasting public goods. Our key programs include: Our Residency is a one to two-year program that supports extraordinary scientists who are producing public goods. We support not only residents’ salaries, but also budgets for them to hire a team and meet other needs to accelerate their work. Radial supports biotechnology-focused projects and is redefining how science should be done. Obelisk does fundamental research in Artificial General Intelligence to ensure it brings good to humanity. Astera is uniquely positioned to shape the course of rapid scientific and technological innovation because of: A $2.5B endowment, which we intend to deploy boldly rather than preserve in perpetuity. A small, agile team: We are staffed much more leanly than other foundations, and aim to keep bureaucracy as minimal as possible. It's more accurate to think of us as a startup than a traditional foundation. High risk tolerance: We take an experimental approach to our programming that means we expect projects to fail with some frequency. Disinterest in consensus: we add value by supporting projects, programs, and people that other organizations can’t or won’t. Location This position is hybrid at our office in Emeryville, CA. Some travel may be required from time-to-time for in-person collaboration and work. Compensation The posted salary range is based on location in the Bay Area. The successful candidate will receive a competitive compensation package, commensurate with their experience and location.

Responsibilities

The researcher will hypothesize, test, and refine methods to improve the generalization performance of common architectural elements, such as different forms of attention, using controlled datasets. They must also contribute to a well-documented and performant code base while thinking both mathematically and empirically about runtime inference in gradient-trained networks.