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Category Theory Scientist - AUS - Categorical Deep Learning

Symbolica AI

Symbolica AI

Posted on Friday, April 26, 2024

At Symbolica, we are building deep learning models which perform structured reasoning: manipulate structured data, learn algebraic structure in it, and do so with an interpretable and verifiable logic. To that end, we are developing new mathematical foundations for deep learning: categorical deep learning. We are now assembling a R&D lab of expert category theory and machine learning researchers to develop this theory and apply it to the problems of code synthesis and theorem proving. We are committed to fundamental ideas, but also their execution in practice.

As a Category Theory scientist, you will help us expand and carry out our research & development program. You will work in a team on a project developing categorical deep learning and implementing it in models. This is an opportunity to be part of a transformative project and make significant contributions to the field of applied category theory and artificial intelligence.


  • Help develop our categorical deep learning research programme
  • Conduct applied category theory research in collaboration with a team, and contribute to high quality results on project timelines
  • Understand state of the art category theory research and help us translate relevant insights into the field of deep learning
  • Work simultaneously at different levels of abstraction - from high-level categorical constructions to low-level details of architecture implementation in code

Preferred qualifications:

  • PhD in Mathematics, Computer Science or similar discipline
  • Industrial or academic work experience post PhD
  • In-depth understanding of category theory or type theory, and a strong interest in machine learning
  • Exceptional communication and interpersonal skills


  • Melbourne (preferred) or AUS remote

We offer competitive compensation, including equity and health insurance. Salary and equity levels are commensurate with experience and location.