Unconventional AI is rethinking the foundations of compute to bring biology-scale energy efficiency to AI. Backed by Lightspeed, Sequoia and a16z.
About the role
As a Member of Technical Staff, Language & Reasoning Models, you will drive the development of foundational language and reasoning models that fundamentally leverage the dynamics of our novel silicon. Your goal is to map the behaviors of modern language models directly onto the physics of our hardware.
What they're looking for
- Education: An MS/PhD or equivalent research/project experience in a quantitative field such as AI/Machine Learning, Computer Science, Physics, Electrical Engineering, or Applied Math
- Experience: Deep, hands-on expertise in the theory, architecture, and training of modern foundation models (transformers, SSMs, text diffusion/flow, etc.)
- Software Development: You are fluent in modern deep learning frameworks (PyTorch or JAX) and have a proven track record of writing clean, scalable training code for large language models
More about this role
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, Language & Reasoning Models, you will drive the development of foundational language and reasoning models that fundamentally leverage the dynamics of our novel silicon. Your goal is to map the behaviors of modern language models directly onto the physics of our hardware.
You will sit at the intersection of NLP/reasoning research and hardware codesign, proving that high-fidelity, large-scale language understanding and generation can be achieved natively on an unconventional computing substrate.
- Model...
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