Research Engineer, Machine Learning

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 engineers support Basis’ mission by translating research ideas into correct, robust, and scalable high-quality code.

We seek individuals who excel technically and value probing concepts at their foundations. Our research engineers aspire to conduct rigorous, high-quality, robust science, unafraid to tinker, make mistakes, and explore radically different ideas to achieve this.

Basis is a collaborative endeavor, both internally and with our external partners; we seek individuals who relish working with others on challenges larger than those they can tackle alone.

This role targets experts in machine learning engineering. The core areas of ML research engineering include:

• Probabilistic programming and statistical inference

• Deep learning

• Causal inference

• Program synthesis and analysis

• ML Ops and systems engineering

These areas are honed within the context of building reasoning systems. Consequently, research engineers will also engage with topics such as programming language design and implementation, automatic differentiation, and SAT/SMT solvers, among others.

• Possess excellent programming and software engineering skills, especially in Julia, Python, C++, ML-family languages.

• Have demonstrated the ability to drive software projects from start to finish. This could be evidenced by open-source projects, technical reports, and publications.

• Be comfortable digesting research from PL and/or ML venues, such as PLDI, POPL, NeurIPS, or ICML.

• Progress with a high degree of autonomy and under uncertainty.

• Be enthusiastic about solving real-world problems and making a positive societal impact.

• Have demonstrated significant technical achievements within ML engineering. Examples include:

  • You've implemented variants of newly published techniques from scratch.

  • You built systems and workflows for training large models distributed across many machines.

  • You've built systems that span all levels of the programming stack from high-level API infrastructure to close-to-the-metal code.

• A PhD (or equivalent experience) in technical areas including: statistics, programming languages, machine learning, computational neuroscience, cognitive science, physics, mathematics.

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  Translate research ideas into correct, robust, and scalable high-quality code.

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  Engage in programming language design/implementation.

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  Performance engineering, scaling research code.

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  Algorithm development.

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  Contribute to the culture and direction of Basis.

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

Exceptional candidates who may not meet all of the following criteria are still encouraged to apply.

• FT/PT: This is a full-time position

• Hours: While we prioritize in-person collaboration for its benefits to creative work, there is a degree of flexibility in your working hours. Be prepared to attend multi-day Basis-wide in-person events.

• Location: This role is in-person in either New York City or Boston.

• Salary range: Competitive salary and bonuses

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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