traceoid.ai (http://traceoid.ai) is revisiting the theory that underlies machine learning. Our approach opens up the door towards scalable energy-based models, faster and cheaper training and inference and interpretability. If we succeed, our approach will mark a significant step forward towards achieving AGI. Here is what Yann LeCun had to say about EBMs https://x.com/ylecun/status/1380066315600343042.
Please reach out, if you have background in any of these fields (or are excited to learn):
- integrable systems
- algebraic topology (in particular Hopf algebras, tensor categories)
- quantum groups
- category theory
- statistical mechanics
- computational physics (see the link above for a full list).
Our company operates at the intersection of research & development, and as a part of this job, you will be reading papers and implementing some very cool research from the above fields. You will be joining a team with several PhD's and while advanced degrees are not a hard requirement, you are expected to possess deep knowledge of the above fields. Ideal candidate would understand how one goes about writing high perf code as well as be able to explain the concept of a Hamiltonian. Contact us at adam+jobs@traceoid.ai.
You can also join our Discord channel https://discord.com/invite/mr9TAhpyBW even if you just want to follow along.