Research Scientist

What We're Building at Simplex

At Simplex, we're building a science of intelligence. Our aim is to develop and apply a rigorous theory of latent internal structure in neural networks, and how that structure relates to computation and behavior.

We believe that when dealing with intelligence, understanding is safety. Without genuine understanding, we can't reliably monitor, control, or even reason clearly about what these systems are doing. But these same systems also present us with a new opportunity. For the first time, we have AI complex enough to serve as testbeds for theories of intelligence, including biological. We aim to build a theory applicable to intelligence, both artificial and biological.

We have the beginnings of such a theory, grounded in the physics of information and experimentally verified in transformers. Now, we are scaling our team. Some of the near-term goals we have are building unsupervised methods that recover belief geometries in real LLMs, extending the theory to more complex cognitive tasks, and pushing toward tools reliable enough to matter for safety.

We're looking for people who can do rigorous mathematics and get their hands dirty with real models and data; ideally someone who moves naturally between theory and experiment, and feels deeply driven to understand intelligence.

You learn across fields. Our work draws on many fields, like dynamical systems, probability, deep learning, physics, information theory, and neuroscience. You don't need to know all of it coming in, but you're the kind of person who picks things up, and follows your curiosity—and surprising experimental results—wherever they lead.

You have taste. You know the difference between a problem that matters and a problem that’s merely publishable. You have opinions about research directions, not just techniques. You care about the craft of how experiments are designed, analyzed, and presented.

You're self-directed. We're a small team. You'll have real ownership over your work, which means figuring out what to do, not just how to do it. You'll work closely with the research team, but we expect you to develop and pursue your own ideas within the broader research program.

You communicate. You can explain your ideas clearly to collaborators, in writing, and on a whiteboard. Science is a team activity for us, and that requires being able to think together.

You build. You're at home in front of a whiteboard and in a terminal. We are building new theory, new code, and new experiments. You think big, but you're serious about it, and you actually try to make things happen rather than just ideating. You use AI tools, you tinker, you're excited about what's becoming possible.

You have depth in at least one quantitative field such as physics, mathematics, neuroscience, machine learning, etc. A PhD is typical but not required if you've found another way to go deep.

Belief discovery at scale

Finding belief-state geometry in large language models without supervision. Can we automatically identify the internal structures that encode what a model knows about the world?

Building a theory of intelligence

We have the beginning steps of a theory, but it needs to be extended and refined in a number of ways, in order to, e.g., capture internal world models of different types and apply to other neural systems (e.g., RL and biological brains).

Generalization

Why and how do neural networks generalize? Our framework suggests ways in which internal structures support out-of-distribution behavior.

Red Teaming

We have an entire team dedicated to stress-testing our own framework. Finding the boundaries, the edge cases, the places where the theory breaks down in service of figuring out what's actually true.

Biological Intelligence

The same mathematics that reveals structure in transformers might apply to biological neural networks. We plan on testing this on real brain data because ultimately we’re interested in intelligence wherever it appears.

Our foundational result (manuscript, blog post) showed that transformers trained on next-token prediction spontaneously organize their…