Basis is a nonprofit applied AI research organization with two mutually reinforcing goals.
The first is to understand and build intelligence. This means to establish the mathematical principles of what it means to reason, to learn, to make decisions, to understand, and to explain; and to construct software that implements these principles.
The second is to advance society’s ability to solve intractable problems. This means expanding the scale, complexity, and breadth of problems that we can solve today, and even more importantly, accelerating our ability to solve problems in the future.
To achieve these goals, we’re building both a new technological foundation that draws inspiration from how humans reason, and a new kind of collaborative organization that puts human values first.
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.
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.
For this role, we are specifically looking for experts in Reinforcement Learning & Planning who can advance the state of the art in model-based RL, exploration strategies, optimal control, and Bayesian optimization. You will work on developing agents that can learn efficient policies in complex, partially observable environments by leveraging structured world models.
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 reinforcement learning, planning, MDPs, optimal control, and sequential decision making.
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 reinforcement learning, planning, and decision-making in AI systems.
Apply these methods to concrete challenges such as AutumnBench, physical and simulated robotics environments, 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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