Research Scientist, World Models

Research scientists lead Basis' efforts to develop a deeper understanding of the conceptual, mathematical, and computational principles of intelligence.

We are looking for people who are technically excellent, and who value probing concepts at their foundations. Our research scientists/engineers aspire to do rigorous, high-quality, robust science, but are not afraid to tinker, make mistakes, and explore radically different ideas in order to get there.

Basis is a collaborative effort, both internally and with our external partners; we are looking for people who enjoy working with others on problems larger than ones they can tackle alone.

Despite the increasing recognition that both having and discovering world models are central to intelligence, current AI systems struggle to replicate this human capability. There remains significant uncertainty about what precisely constitutes a world model, how we might reliably detect if an agent possesses one, and crucially, how we can develop agents that learn these models rapidly and reliably.

Our research within the MARA project aims to develop new foundations and technologies for modeling, abstraction, and reasoning in AI systems. MARA's overarching goal is to uncover principled methods for how intelligence constructs, refines, and utilizes world models through interactive experimentation. Building these systems will demand advances in knowledge representation, abstraction, reasoning, active learning, reinforcement learning, and a first-principles rethinking of what it means to model the world.

The immediate mission of MARA is to solve concrete challenges such as AutumnBench , physical and simulated robotics benchmarks, and the Abstract Reasoning Corpus (ARC), with the broader mission of building systems capable of learning in an open, growing portfolio of domains using human-comparable amounts of data and interaction.

- Researchers holding a PhD in computer science, artificial intelligence, machine learning, cognitive science, or related fields.

- Strong background in areas such as program synthesis, probabilistic programming, machine learning, AI reasoning systems, and cognitive modeling.

- Experience in developing AI systems that combine neural and symbolic methods is highly valued.

- Interest in foundational AI research and its applications to modeling, abstraction, and reasoning.

- Individuals with a demonstrated track record in scientific research, evidenced through publications, technical reports, or impactful software projects.

- Excited about solving real world problems and having positive societal impact.

- Conduct independent and collaborative research focused on the MARA project.

- Develop new methods and algorithms for modeling, abstraction, and reasoning in AI systems.

- Apply these methods to concrete challenges such as AutumnBench , physical and simulated robotics environments, the Abstract Reasoning Corpus (ARC), and other domains.

- Disseminate research findings through academic publications and presentations at leading conferences.

- Provide mentorship to junior team members and contribute to the scientific discourse through seminars, workshops, and collaborative projects.

- Develop and maintain open-source software

- (Optionally) Publish and present findings in journals and conferences

- Contribute to the culture and direction of Basis

Exceptional candidates who may not meet all of the following criteria are still encouraged to apply.

- FT/PT: This is a full-time position

- In-person Policy: We are in the office four days a week. Be prepared to attend multi-day Basis-wide in-person events.

- Location: This role is in-person in either New York City or Cambridge, MA.

- Salary range: Competitive salary.

- Start date: Immediate start possible.

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