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, AI Systems, Model Optimization, you will develop the path from model architecture to physical silicon. You will develop the training techniques, optimization strategies, and infrastructure required to make AI models run efficiently on our novel compute substrates, closing the loop between model design and tapeout.
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
- Software Development: Deep experience with PyTorch, including its internals, torch.compile, and distributed data parallel (DDP) / fully sharded data parallel (FSDP) libraries
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, AI Systems, Model Optimization, you will develop the path from model architecture to physical silicon. You will develop the training techniques, optimization strategies, and infrastructure required to make AI models run efficiently on our novel compute substrates, closing the loop between model design and tapeout.
- Energy Benchmarking & Performance Modeling: Develop rigorous performance models to evaluate compute, memory, and energy trade-offs. Track pareto-optimality across models and hardware...
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