Join Traceoid to work on scalable energy-based models and advanced machine learning theories. Ideal candidates should have deep knowledge in fields like integrable systems, algebraic topology, quantum groups, category theory, statistical mechanics, and computational physics. The role involves reading papers and implementing research, with a preference for candidates who can write high-performance code and explain concepts like Hamiltonians. Advanced degrees are not required but deep knowledge is expected.
We are working on making energy-based models (EBMs) viable by revisiting some of the math that underlies machine learning. 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 fields such as integrable systems, category theory, functional programming, compilers, statistical mechanics, and computational physics. If we succeed, our approach will mark a significant step forward towards achieving AGI.
