AI Systems, Language & Reasoning Models

About the role

Unconventional AI is rethinking computing to build a foundation for AI that is 1000x more efficient. The company uses semiconductor physics, mapping neural networks directly to device physics rather than relying on layers of inefficient abstraction.

As a Member of Technical Staff, Language & Reasoning Models, you will develop foundational language and reasoning models that leverage the dynamics of novel silicon. The goal is to map modern language model behaviors directly onto the physics of the hardware and demonstrate high-fidelity, large-scale language understanding and generation on an unconventional computing substrate.

Responsibilities

• Design, train, and scale language and reasoning architectures, including transformers, state space models, diffusion/flow models, and deep equilibrium models, tailored for unconventional compute.
• Rethink sequence modeling to exploit the continuous-time dynamics of silicon and move away from layers of inefficient digital abstraction.
• Establish training recipes, loss functions, and evaluation metrics for language comprehension, logical reasoning, and generation speed while maintaining the platform's energy efficiency.
• Collaborate with hardware designers, theorists, and system builders.

Key facts

• The role focuses on developing language and reasoning models for unconventional compute.
• Responsibilities include designing, training, and scaling model architectures.
• The role involves collaboration with hardware designers, theorists, and system builders.