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, System Modeling (Computation), you will be part of a hands-on R&D team building simulation frameworks that enable evaluation and rapid iteration across all layers of unconventional physics-based computing systems for machine learning workloads. “Extreme co-design” is our guiding principle.
What they're looking for
- Education
- MS/PhD in a quantitative field (AI/ML, Computer Science, Physics, Electrical Engineering, Applied Math), or BS with substantial, clear evidence of equivalent research/engineering depth
- Scientific Machine Learning (SciML) & Numerical Methods (replaces Dynamical computation knowledge)
- Deep expertise in numerical differential equation solvers (e.g., ODE, SDE, DDE) and their implementation on parallel architectures (e.g., Rosenbrock methods, Euler-Maruyama, adjoint methods, implicit solvers)
- Experience with high-performance, customized GPU kernel development for numerical methods, including GPU memory optimization and multi-GPU scaling
- Experience building effective neural network surrogate models (e.g., Neural ODEs) for complex dynamical systems
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, System Modeling (Computation), you will be part of a hands-on R&D team building simulation frameworks that enable evaluation and rapid iteration across all layers of unconventional physics-based computing systems for machine learning workloads. “Extreme co-design” is our guiding principle.
System Modeling is a multi-disciplinary effort, and the team we’re building reflects that. The role involves development of physics-based system models, GPU-accelerated ML system simulations, and cross-layer system...
Browse similar: AI jobs · AI startup jobs · Startup jobs · Remote jobs · San Francisco Bay Area