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Principal Machine Learning Scientist - AUS - Categorical Deep Learning

Symbolica AI

Symbolica AI

Software Engineering
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 principal machine learning scientist, you will help us expand, refine, and carry out our research & development program. You will help design internal company procedures, assist with financial planning, and staff teams developing and implementing the theory of categorical deep learning. This is a rare opportunity to work on an innovative and transformative project, and make significant contributions to the field of artificial intelligence and applied category theory. This is a high-impact role in which you will help shape the company; we are looking for advanced researchers and skilled leaders.


  • Lead the development and ownership of pioneering research projects in categorical deep learning, from conceptualisation to execution, while ensuring alignment with Symbolica's strategic objectives
  • Assist with staffing, financial planning and public engagement of the company
  • Work closely with category theorists and machine learning researchers, bridging the gap between state of the art research on deep learning architectures and their structural formulation in category theory
  • Stay at the forefront of experimental advances in deep learning, and ensure our theoretical models are grounded and coherent with respect to these advances
  • Grow, lead and mentor a team of category theorists and machine learning researchers
  • Lead a team prototyping and validating theoretical models in code, demonstrating practical feasibility
  • Work simultaneously at different levels of abstraction - from understanding high-level categorical constructions to implementing low-level details of architecture in code

Preferred qualifications:

  • PhD in Computer Science, Mathematics, or similar discipline.
  • 5+ years of industrial or academic work experience post PhD
  • Proven track record of research published in top-tier conferences (e.g. NeurIPS, ICML, ICLR, AAAI, COLT), and journals
  • Experience with functional programming languages (e.g. Haskell, Idris, Scala)
  • Deep expertise in neural network architectures and a strong interest in category theory or type theory
  • Industrial or academic experience leading a research/technical team, and implementing novel machine learning architectures
  • Experience establishing the culture of early-stage organisations.
  • 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.