Research Intern, Summer 2026

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 interns work closely with scientists and engineers at Basis, learning from and contributing to our team. Internships are open to graduate-level candidates (or equivalent), pursuing research in technical and scientific domains, including areas of computer science such as machine learning, and programming languages.

We are looking for people who want to grow technically and who value gaining a deeper understanding of concepts at their foundations.

We expect you to:

• Have demonstrated an ability to do high-quality scientific research. Possible ways to demonstrate this include publications, technical reports, and software projects

In addition, the following would be an advantage:

• Excited about solving real world problems and having positive societal impact

Activities

• Develop and explore computational theories of intelligence, including reasoning, learning, and decision-making

• In close collaboration with domain experts inside and especially outside Basis, work as a team member to help solve scientific and societal problems

• Distill insights from solving problems into more general mathematical and computational theories

• Contribute to open-source software

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

• Contribute to the culture and direction of Basis

Interns will be placed with one of our active projects, according to project needs and intern fit. In particular, we have openings within the following areas:

• Dynamical Systems + Machine Learning at Basis

  • We're seeking interns interested in developing the theory and methods that let us learn how systems work from data—and quantify what we don't know. This work sits at the intersection of machine learning, dynamical systems, and uncertainty quantification, with the goal of advancing how we represent, infer, and forecast complex real-world processes under uncertainty.

  • Projects may be theoretical (developing foundations for learning and reasoning about dynamics), methodological or engineering-focused (building and integrating algorithms into our universal reasoning engine), or applied (using our existing toolkits to study systems such as animal behavior and robot co-design).
    Interns should have familiarity with differential equations, machine learning, and Bayesian inference.

• Collaborative Intelligent Systems

  • We study collaborative intelligent systems in the wild, focusing on how social species coordinate, communicate, and adapt in complex environments. Our work spans multiple levels of behavior, from fine-grained group foraging and navigation decisions to city-scale ecological and evolutionary dynamics. Using multimodal data (audio, video, environmental, and genetic), we develop probabilistic and dynamical systems models that investigate how communication and cooperation shape resilience in changing ecosystems. Interns will contribute to data analysis, modeling, or field data collection for our "behavioral weather station" network monitoring social species across cities.

  • Key skills: interest in animal behavior or collective systems; experience with data science, machine learning. Preference will be given to candidates with experience in at least one of the following areas: dynamical systems modeling, multimodal data analysis, multi-agent simulation.

• MARA – Modeling, Abstraction & Reasoning Agents

  • Science advances by discovering useful abstractions. MARA operationalizes this insight into software agents. Instead of passively absorbing data, MARA agents propose hypotheses, run physical or simulated experiments, and revise models of the world until a compact, coherent theory emerges. Early results show MARA systems solving previously unresolved (ARC) Abstraction and Reasoning Corpus tasks; widely considered among the hardest AI benchmarks today. We are also opening a new branch of the project to bring MARA into the physical world, with embodied agents (robots) that learn from experience, build internal representations, and apply "everyday science" human-like reasoning to solve problems. Read the latest from MARA on our blog.

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

• Start and end dates: This is typically a summer position but the dates are flexible. The internship should span at least three months.  

• Location: This is an in-person position in either the New York City or Boston/Cambridge area.

• Salary: $100,000 annually

• Application close date: 

  • We will begin reviewing applications for Summer 2026 in October, and the application will remain open until the cohort is full. 

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