About the job
About Basis
Basis is a nonprofit organization dedicated to applied artificial intelligence research. Our mission is twofold: to understand and build intelligence and to advance society’s problem-solving capabilities. We strive to unravel the mathematical principles behind reasoning, learning, decision-making, understanding, and explanation, while also developing software that embodies these principles.
Our commitment extends to enhancing our ability to tackle complex issues that are beyond today's capabilities and accelerating our potential to address future challenges. We are creating a technological framework inspired by human reasoning, alongside fostering a collaborative organization that prioritizes human values.
About the Role
As a Research Scientist, you will spearhead Basis’ initiatives to deepen our understanding of the conceptual, mathematical, and computational principles of intelligence. We seek individuals who excel technically and are passionate about exploring foundational concepts. Our research team values rigorous, high-quality scientific endeavors while encouraging experimentation and exploration of innovative ideas.
Basis thrives on collaboration, both internally and with external partners. We are looking for team players who enjoy tackling significant problems that require collective effort.
Research Focus
Despite the growing acknowledgment that acquiring and understanding world models is crucial for intelligence, current AI systems face challenges in mirroring this human capability. Key uncertainties remain regarding the essence of a world model, methods for reliably detecting its presence in agents, and approaches to develop agents capable of learning these models effectively.
Our research, particularly within the MARA project, seeks to establish new foundations and technologies for modeling, abstraction, and reasoning in AI systems. MARA's goal is to identify principled methods for how intelligence constructs, refines, and employs world models through interactive experimentation. Achieving this will require advancements in knowledge representation, abstraction, reasoning, active learning, and reinforcement learning, necessitating a first-principles reevaluation of world modeling.

