System Modeling (Computation)

Unconventional AI is a company focused on redefining computing to overcome energy limitations in AI. They are seeking a Member of Technical Staff for System Modeling (Computation) to develop simulation frameworks for evaluating and optimizing unconventional physics-based computing systems for machine learning workloads. Responsibilities Architecting Foundational Solvers: Building large-scale, GPU-accelerated, high-fidelity numerical differential equation solvers (ODE, SDE, CDE, PDE). You will build tools that enable rapid iteration, multiple architectures, and rich metrics/visualization, leveraging frameworks best suited for scientific ML (e.g., JAX, PyTorch, or custom CUDA/Triton kernels) Bridging Physics and Machine Learning: Developing physics-based surrogate models of device- and system-level behavior in unconventional compute. You will create clean, composable abstractions that expose algorithm–hardware tradeoffs and enable cross-layer optimization via end-to-end autodiff Extreme Co-Design & Collaboration: Working closely with hardware and algorithm teams to understand their simulation needs, supporting everything from high-level algorithm development to the low-level verification of novel, analog hardware