Machine Learning Researcher - Springtail
About Astera
Astera is a private foundation on a mission to steer science and technology toward an abundant future. We believe the coming years will bring an era of unprecedented scientific and technological advancement as exponential progress in AI converges with central advances in other fields to dramatically accelerate innovation. This inflection point provides an unparalleled opportunity to fundamentally rethink the institutions, systems, and tools that drive scientific progress.
Unlike traditional non-profit research organizations, Astera funds and operates projects like high-velocity startups, allowing us to focus on ambitious goals, match structure to problem, and attract strong technical talent and leadership. You can read more about our mission, vision, and programming here.
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.
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.