Research Engineer, Machine Learning

About the role

ABOUT BASIS

Basis https://www.basis.ai 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 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.

Machine Learning Research Engineers

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.

We expect you to:

- 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.