AI Hardware Architecture
Unconventional AI
AI Hardware Architecture
Unconventional AI
Analog computing substrate for energy-efficient AI
Since 2022, AI has entered the mainstream, reshaping entire industries from education and software development to fundamental consumer behaviors. This revolution has created an unprecedented demand for computation - a demand that is now fundamentally limited by energy, not just in the datacenter, but at a global scale.
At Unconventional, our mission is to solve this. We are rethinking computing from the ground up to build a new foundation for AI that is 1000x more efficient. We're doing this by exploiting the rich physics of semiconductors, mapping neural networks directly to the device physics rather than relying on layers of inefficient abstraction.
As a Member of Technical Staff, you will be a foundational member of our small, multi-disciplinary R&D team. This isn't a role with a predefined checklist; it's an opportunity to help define the "what" and the "how."
We are looking for 'first principles' thinkers who are excited to tackle the hardest, most ambiguous technical challenges at the intersection of AI, physics, and computer architecture. You will be responsible for driving invention, prototyping, and validation of the core components of our novel computing platform.
Your work will be fluid and could span from theoretical modeling and simulation to algorithm development, hardware/software co-design, or experimental validation in collaboration with other team members. We're hiring exceptional problem-solvers who can navigate deep uncertainty and help chart our technical roadmap.
We are seeking exceptional individuals who will help to propose, design, and evaluate analog-computing hardware architectures for achieving a 1000x energy-efficiency advantage in ML inference.
The ideal candidate will have a deep understanding of the principles and engineeringpractice of analog and/or other unconventional physics-based computing paradigms and a proven ability to design and evaluate analog/unconventional computing hardware architectures.
Analog computing substrate for energy-efficient AI
Unconventional AI designs analog AI hardware and silicon substrates that run neural networks directly in hardware. Its chips use analog circuits that mimic biological neuron dynamics to process AI workloads in the physics of silicon, reducing energy use compared with digital GPUs. It differentiates itself by pursuing an in-hardware, neuromorphic-style substrate rather than optimizing existing GPUs, supported by prominent venture funding. Its goal is to counter the AI energy bottleneck and enable scalable AI in data centers with biology-scale efficiency.
Explore more opportunities at Unconventional AI