Research Scientist, Machine Learning

About the role

This role deepens the mathematical and computational foundations of intelligence and expands society's capacity to solve difficult problems. Research scientists partner across teams and organizations, while the role also shapes the culture and direction of Basis as a values-first collaborative effort. You will interrogate the core mechanisms of cognition by constructing formal models that translate abstract principles into testable predictions. The position requires you to synthesize insights from mathematics, computation, and human behavior to address questions that are fundamentally about the nature of understanding. You will be responsible for converting high-level philosophical inquiries into structured research agendas that can be validated through experimentation and analysis. Your work will establish the theoretical scaffolding that allows empirical findings to generalize across diverse domains and contexts. Ultimately, you will ensure that the methodologies developed at Basis remain robust, interpretable, and aligned with long-term societal benefits.

Key facts

What you'll do

Formulate research questions that dissect the principles of reasoning, learning, and decision-making to construct rigorous theoretical frameworks. Translate abstract scientific inquiries into concrete computational experiments that validate the scalability and reliability of proposed models. Partner with internal specialists and external collaborators to decompose large, intractable problems into manageable subcomponents with clear interfaces. Engineer open-source software artifacts that encapsulate research findings, ensuring they are documented, tested, and accessible for reuse by the broader community. Analyze complex datasets to extract signals that inform theoretical advances, iterating on models until they exhibit strong empirical and conceptual fit. Communicate intricate methodologies through academic publications and conference presentations to maintain alignment with the wider research ecosystem. Evaluate the societal implications of technical results, ensuring that solutions contribute to positive outcomes and ethical standards of practice. Guide the strategic direction of Basis by integrating empirical evidence with long-term philosophical commitments to collaborative progress. Maintain a rigorous standard of verification that distinguishes robust conclusions from speculative hypotheses in the study of intelligence.

Requirements

Demonstrate high-quality scientific research ability, with evidence such as publications, technical reports, or software projects that show rigor and robustness. Meet educational expectations with a PhD or equivalent experience in statistics, programming languages, machine learning, computational neuroscience, cognitive science, physics, or mathematics. Possess the analytical maturity to deconstruct complex systems and identify the minimal set of assumptions required for valid conclusions. Drive toward real-world impact by solving concrete problems and creating positive societal outcomes through technically sound methodologies. Prepare for intensive, multi-day Basis-wide gatherings that demand deep focus and sustained intellectual contribution. Work effectively within an in-person schedule that prioritizes collaboration in New York City or Cambridge, MA, with flexibility maintained for hybrid arrangements when feasible. Engage with ambiguity by formulating clear definitions for vague problems and maintaining clarity about uncertainty in the face of incomplete information. Communicate results in a manner that balances precision with accessibility, ensuring that technical arguments are understandable to interdisciplinary audiences.

Practical notes

This position is based in-person in New York City or Cambridge, MA, where in-person collaboration is prioritized for creative work and flexibility is maintained for hybrid arrangements. Participation in multi-day Basis-wide events is expected to align with team needs. Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

The role centers on research at the intersection of machine learning and cognitive science. Work involves building and testing computational models of reasoning and decision-making. Open source software development is integrated into the research workflow. Collaboration amplifies problem solving on complex, large-scale problems. The position engages with both theoretical foundations and real-world applications.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.