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
We are rethinking the foundations of the computer to optimize energy efficiency for AI. Founded by pioneers in the field - including Naveen Rao (Nervana, MosaicML) and Michael Carbin (MIT, MosaicML) - we are building a new computational substrate that interfaces directly with the physics of silicon to achieve biology-scale efficiency. We recently raised $475M in seed funding to turn this vision into reality.
What they're looking for
- Strong Systems Foundation: A BS, MS, or PhD in Computer Science, Electrical Engineering, or a related technical field. You should have a deep understanding of computer architecture and operating systems
- Coding Proficiency: Strong skills in C++ and Python . You should be comfortable writing performance-critical code
- AI/ML Exposure: Basic familiarity with the internals of deep learning frameworks (e.g., how a PyTorch graph is executed) and common model architectures
- Mathematical Intuition: A solid grasp of linear algebra and calculus, which are essential for understanding both neural dynamics and hardware optimizations
- First Principles Mindset: You enjoy digging into "why" things work (or don't) and aren't afraid to challenge conventional software "best practices" to find a more efficient path
More about this role
We are rethinking the foundations of the computer to optimize energy efficiency for AI. Founded by pioneers in the field - including Naveen Rao (Nervana, MosaicML) and Michael Carbin (MIT, MosaicML) - we are building a new computational substrate that interfaces directly with the physics of silicon to achieve biology-scale efficiency. We recently raised $475M in seed funding to turn this vision into reality.
As a Junior Member of Technical Staff, System Modeling , you will work closely with senior engineers to contribute to the development of our multi-disciplinary simulation frameworks. You will assist the hands-on R&D team in building simulation environments that enable rapid iteration and testing across all layers of our unconventional physics-based computing systems for machine learning workloads. Your work will focus on integrating physics-based models, developing GPU-accelerated simulations, and supporting the cross-layer system integration necessary for "Extreme co-design".
- Contribute to the implementation and optimization of GPU-accelerated simulators for ML on analog/unconventional hardware, focusing on specific modules and features within PyTorch.
- Assist in...
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