AI Systems, Language & Reasoning Models

Unconventional AI is focused on redefining computing to address the energy limitations of AI at a global scale. The AI Systems, Language & Reasoning Models role involves developing foundational language and reasoning models that leverage novel silicon dynamics, collaborating closely with hardware designers to achieve efficient high-fidelity language understanding and generation.

Responsibilities:

• Design, train, and scale next-generation language and reasoning architectures (such as transformers, state space models, diffusion/flow models, and deep equilibrium models) specifically tailored for unconventional compute
• Rethink standard sequence modeling to exploit the continuous-time dynamics of silicon, moving away from layers of inefficient digital abstraction
• Establish the training recipes, loss functions, and evaluation metrics needed to reach the frontier of language comprehension, logical reasoning, and generation speed while maintaining the massive energy efficiency of our platform
• Collaborate with hardware designers and theorists, and system builders to co-design the model architecture alongside the underlying physical compute primitives