Research Scientist, Program Synthesis & Neuro-symbolic Methods
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 on the MARA (Modeling, Abstraction, and Reasoning Agents) project develop computational theories of scientific reasoning applied to robotics and embodied intelligence. You will advance the frontiers of world modeling, reinforcement learning, program synthesis, and robotic control to create systems that can learn, reason about, and interact with the physical world.
We are looking for exceptional researchers with expertise in Program Synthesis & Neuro-symbolic Methods. The ideal candidate has a strong publication record in relevant venues, combines theoretical depth with practical implementation skills, and is excited about building systems that learn like scientists-forming hypotheses, conducting experiments, and building models of how the world works.
You will work as part of an interdisciplinary team tackling fundamental questions: How can agents learn causal models from interaction? How do we bridge high-level reasoning with low-level control? How can we generate interpretable, verifiable control programs rather than black-box policies?
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.
We expect you to:
Have demonstrated an ability to do scientific research that is of high quality. Possible ways to demonstrate this include publications at top venues (NeurIPS, ICML, ICLR, POPL, PLDI), technical reports, and impactful software projects.
Possess deep expertise in Program Synthesis & Neuro-symbolic Methods:
Domain-specific languages, program induction, verifiable control, neuro-symbolic integration.
Experience with combining neural networks with symbolic reasoning or program generation.
Have strong mathematical and computational foundations including probability theory, optimization, linear algebra, and the ability to implement complex algorithms from first principles.
Be comfortable working across the research-to-deployment pipeline, from theoretical development through experimental validation.
Progress with autonomy and intellectual curiosity. You can identify valuable research directions within the broader MARA mission, design experiments, and drive projects to completion.
Value collaboration and knowledge transfer. You actively share insights across specialization boundaries and help integrate diverse approaches into coherent systems.
Be excited about solving real-world problems through embodied intelligence that advances our ability to understand and interact with the physical world.
In addition, the following would be an advantage:
PhD (or equivalent experience) in technical areas including: robotics, machine learning, computer vision, control theory, cognitive science, or physics.
Experience at leading robotics or AI labs (academic or industry).
Track record of algorithms deployed on physical robot systems.
Contributions to major open-source projects in robotics or ML.
Experience with both theoretical research and systems engineering.
Background spanning multiple specialization areas.
Responsibilities
Develop computational theories of intelligence specific to program synthesis and neuro-symbolic methods, focusing on synthesizing control programs, learning interpretable models, or bridging symbolic reasoning with neural learning.
Design and implement novel algorithms that push the boundaries of sample efficiency, generalization, interpretability, or robustness in embodied AI systems.
Collaborate across specializations to integrate world modeling with planning, symbolic reasoning with neural learning, and high-level objectives with low-level control.
Validate research on physical systems by working with hardware engineers to test algorithms on real robots, addressing the sim-to-real gap and practical deployment challenges.
Work with domain experts inside and outside Basis to identify impactful applications of MARA technology in scientific discovery, manufacturing, or other domains.
Distill insights from problem-solving into general mathematical and computational theories that advance our understanding of intelligence.
Develop and maintain open-source software that enables reproducible research and broader community engagement with MARA technologies.
(Optionally) Publish and present findings in journals and conferences to establish thought leadership in embodied AI and scientific reasoning.
Contribute to the culture and direction of Basis by modeling scientific rigor, creative problem-solving, and commitment to advancing societal capabilities.
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.
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