Unconventional AI
System Modeling (Computation)
Mountain View, CA I US Remote +2 · Remote · Full-time · Posted Apr 1, 2026
- Location
- Mountain View, CA I US Remote
Mountain View
Remote - Education
- MS/PhD in a quantitative field, or BS with substantial, clear evidence of equivalent research/engineering depth.
About the job
Build simulation frameworks and physics-based models for unconventional computing systems, enabling evaluation and optimization of machine-learning workloads across hardware and software layers.
What you'll do
- Develop GPU-accelerated numerical differential-equation solvers and simulation tools.
- Create physics-based surrogate models that support end-to-end autodiff and cross-layer optimization.
- Collaborate with hardware and algorithm teams on simulation, algorithm development, and verification of analog hardware.
What they ask for
- Deep expertise in numerical differential-equation solvers and implementation on parallel architectures.
- Experience developing customized high-performance GPU kernels for numerical methods, including memory optimization and multi-GPU scaling.
- Experience building neural-network surrogate models for complex dynamical systems.
- Solid understanding of AI/ML architectures and workflows, plus experience implementing, debugging, profiling, and optimizing ML systems in PyTorch or similar.
Nice to have
- Exceptional Python engineering, API design, and experience building scientific software or simulation frameworks.
- Experience with compiler-friendly ML paradigms, analog dynamic systems, cross-layer systems reasoning, or efficiency techniques.
Tools
JAX, PyTorch, CUDA, Triton, XLA
AI focus
Develop simulation frameworks and physics-based models for machine-learning workloads, including neural-network surrogates and cross-layer hardware–algorithm optimization.
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