System Modeling (Computation)

Unconventional AI is focused on redefining computing to create a more efficient foundation for AI. The role of Member of Technical Staff, System Modeling (Computation) involves developing physics-based system models and GPU-accelerated simulations to support machine learning workloads, fostering collaboration across hardware and algorithm teams.

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