Member of Technical Staff
Unconventional AI
Member of Technical Staff
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 the first hire and zero-to-one owner of our Agentic Applications pod, you will serve as the founding engineer for Unconventional AI’s internal enterprise productivity suite. This is a high-impact, highly autonomous role where you will be the force multiplier for our entire team. You will be responsible for architecting and developing bespoke applications from the ground up using Agentic AI. Your work will directly accelerate our business operations, covering areas such as intelligent talent recruiting tools, dynamic knowledge bases, contextual AI assistants, and semantic search. You won't just be writing code; you will drive the overarching technical strategy, lead "build vs. buy" decisions, and set the foundation for how AI agents augment our day-to-day operations.
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.
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