[Remote] System Modeling (Computation)

Note: The job is a remote job and is open to candidates in USA. Unconventional AI is focused on redefining computing to meet the unprecedented demand for efficient AI solutions. They are seeking a Member of Technical Staff, System Modeling (Computation) to develop simulation frameworks for physics-based computing systems, working collaboratively with 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

Skills

• 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
• 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
• Solid understanding of modern AI/ML architectures and training/inference workflows
• Strong experience implementing and debugging ML models in PyTorch (preferred) or similar, with practical experience profiling, optimizing, and stabilizing non-trivial large-scale ML systems
• Exceptional Python engineering skills with a passion for Developer Experience (DX), elegant API design, strong typing, and composability
• Experience with compiler-friendly ML paradigms and internals (e.g., JAX vmap/pmap/jit, PyTorch autograd/torch.compile, custom XLA or Triton kernels)
• A track record of building open-source tools, scientific libraries, or serious simulation/modeling frameworks from scratch
• Familiarity with analog dynamic systems, including transient responses, and nonidealities such as nonlinearity, quantization, random noise, and feedback/stability
• Demonstrated ability to reason across multiple layers of the stack: algorithm, software, compiler, runtime, and hardware
• Able to cleanly connect model architecture choices to system performance implications (memory bandwidth, communication patterns, latency, energy, and numerical stability)
• Experience applying efficiency techniques natively within modeling frameworks (quantization, sparsity, pruning, distillation, kernel fusion, etc.)

Benefits

• Best-in-class health benefits
• 401k matching
• Truly unlimited PTO
• Complimentary meals when working from our Palo Alto office

Company Overview