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Basis Research Institute

Research Scientist, World Models

New York, United States +1

Location
New York, United States · Cambridge, Massachusetts, United States
Work
On-site
Employment
Full-time
Level
Not stated
Function
Science & research
Pay
$120K – $180K a year
Posted
12 months ago

Pay and the market here

The top of this role’s range, $180K a year, is 22% below the median of $230K for science jobs in New York (middle half $151K – $300K, 183 jobs that state pay).

227 science jobs open in New York: 13% remote, 59 posted in the last 30 days.

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About the role

About Basis

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.

About the Role

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.

Research Focus

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.

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

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 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

Role Details

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.

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.

Privacy Notice

By submitting your application, you grant Basis permission to use your materials for both hiring evaluation and recruitment-related research and development purposes. Your information may be processed in different countries, including the US. You retain copyright while providing Basis a license to use these materials for the stated purposes.

Read our full Global Data Privacy Notice here.

Source

Posted by Basis Research Institute on their own hiring page, jobs.ashbyhq.com. OptiTalent shows it as published, read 07 Nov 2026 11:50 UTC; the original is the reference.

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