Research Scientist/Engineer

About the role

ABOUT ASTERA

Astera is a private foundation on a mission to steer science and technology toward a better 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 accelerate innovation. This inflection point provides an unparalleled opportunity to fundamentally rethink the institutions, systems, and tools that drive progress. We have committed a $3.5B endowment to that work and fit the form of our support to the function it serves: grants, investments, and in-house research programs. We accept meaningful risk in the research we back and expect some of it to fail because transformative ideas are rarely the ones already proven. Our projects, and the foundation itself, operate like high-velocity startups, with a high performance bar and competitive compensation to match. We are searching for leaders who are compelled by the challenge of driving high-impact change in the way science is funded, conducted, and communicated. You can read more about our mission, vision, and programming at astera.org/vision http://astera.org/vision.

POSITION SUMMARY

We are seeking brilliant, unconventional thinkers to join us. In this role, you will not be chasing incremental gains on standard benchmarks. Instead, you will be tasked with developing the foundational principles of Artificial General Intelligence. We believe that understanding the "code of the brain" is the most viable path to building truly intelligent machines, and we are looking for scientists who can bridge the gap between biological intelligence, computational theory, and building at scale.

CORE RESPONSIBILITIES

- Fundamental research on brain-like world models: Develop and implement new architectures and learning algorithms for neuroscience-based world models. You will draw up on insights from deep learning, causality, probabilistic and energy based models, and biologically inspired architectures.

- Scientific Discovery: Design experiments to test hypotheses regarding high-level cognition, causal representations, sensory-motor integration, active inference, and unsupervised learning.

- Cross-Disciplinary Collaboration: Work alongside neuroscientists and software engineers to translate abstract mathematical frameworks into scalable systems.

- Contribute to open science: Help innovate new publication models that incentivize speedy dissemination, open source code releases, free open access, and impact measurements based on uptake.

DESIRED QUALIFICATIONS

- Technical Depth: Preferred: PhD in Computer Science, Electrical Engineering, Neuroscience, Physics, or a related quantitative field. We are always open to considering exceptional candidates, even those with non-traditional educational histories.

- Theoretical Rigor: Strong foundations in deep learning, graphical models, and information theory.

- Coding Proficiency: Expert-level skills in deep learning frameworks (PyTorch/JAX), with a focus on clean, reproducible research code.