Postdoctoral Fellow, MARA (Modeling, Abstraction and Reasoning Agents)

Basis Research Institute; Cambridge (MA); On-site; USD 90,000 - 110,000; Full time

Job summary

Basis Research Institute in Cambridge, MA is offering a full-time Postdoctoral Fellowship focused on the MARA project, aimed at advancing AI technologies. Researchers should hold a PhD in relevant fields and have a strong background in program synthesis and AI reasoning. Responsibilities include conducting research, developing algorithms, and mentoring team members. The position includes competitive compensation of $100K with a duration of 1 to 2 years, requiring collaboration with Cornell University.

Qualifications

• Demonstrated track record in scientific research through publications or technical reports.
• Experience in developing AI systems that combine neural and symbolic methods is highly valued.

Responsibilities

• Conduct independent and collaborative research focused on the MARA project.
• Develop new methods and algorithms for modeling, abstraction, and reasoning in AI systems.
• Disseminate research findings through academic publications and presentations.

Skills

AI reasoning systems

Program synthesis

Probabilistic programming

Cognitive modeling

Interest in foundational AI research

Education

PhD in computer science, artificial intelligence, machine learning, cognitive science, or related fields

Job description

About The Fellowship

This Basis Postdoctoral Fellowship is a collaborative initiative between the Basis Research Institute and Cornell University's Ellis Lab. As a fellow, you will be a key contributor to our ambitious MARA (Modeling, Abstraction, and Reasoning Agent) project, which aims to develop foundational AI technologies that enable systems to actively discover abstract models of the world and reason with them to achieve goals.

About Basis

Basis is a nonprofit applied AI research organization with two mutually reinforcing goals.

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.

Advance society’s ability to solve intractable problems: expanding the scale, complexity, and breadth of problems that we can solve today, and 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.

About Kevin Ellis’ Group

Kevin Ellis is an Assistant Professor in the Computer Science department at Cornell University. His research focuses on artificial intelligence, program synthesis, and the intersection of AI and cognitive science. The Ellis Lab explores how to build AI systems that learn and reason like humans, particularly in areas such as programming by example, world modeling, neural‑symbolic integration, and few‑shot learning. The group combines techniques from machine learning, program synthesis, probabilistic programming, and cognitive science to develop AI systems that can learn complex tasks from limited data and generalize across domains.

Research Focus

Our research aims to develop new foundations and technologies for modeling, abstraction, and reasoning in AI systems, focusing on the MARA project. MARA’s general goal is to build systems that actively discover abstract models of the world and reason with these models to carry out goals. Building these systems will demand advances in knowledge representation, abstraction, reasoning, active learning, and a first‑principles rethinking of what it means to model the world.

The immediate mission of MARA is to solve the Abstract Reasoning Corpus (ARC) in a way that generalizes to other domains, 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.

Who We’re Looking For

• 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.

Core Responsibilities

• 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 the Abstract Reasoning Corpus (ARC) and other domains.
• Disseminate research findings through academic publications and presentations at leading conferences.
• Actively engage in knowledge transfer within Basis and Cornell University, converting research into actionable insights and algorithms.
• Provide mentorship to junior team members and contribute to the scientific discourse through seminars, workshops, and collaborative projects.

Role Details

• Full‑time: This fellowship is full‑time and has a fixed duration of 1 to 2 years.
• Location: This is an in‑person position, with time split between Ithaca, NY and NYC. You will have space at Kevin Ellis’s lab at Cornell University and will collaborate closely with Basis Research Institute. You will be expected to travel periodically, about once every six to eight weeks, for Basis‑wide in‑person events, typically in New York City.
• Salary: Competitive with leading postdoctoral fellowships.
• Start date: Immediate start possible.

Compensation

Compensation Range: $100K

Non‑Discrimination Notice

Basis Research Institute provides equal employment opportunities without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or genetics and prohibits discrimination based on all protected characteristics.