[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.ResponsibilitiesArchitecting 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 autodiffExtreme 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 hardwareSkillsMS/PhD in a quantitative field (AI/ML, Computer Science, Physics, Electrical Engineering, Applied Math), or BS with substantial, clear evidence of equivalent research/engineering depthDeep 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 scalingExperience building effective neural network surrogate models (e.g., Neural ODEs) for complex dynamical systemsSolid understanding of modern AI/ML architectures and training/inference workflowsStrong experience implementing and debugging ML models in PyTorch (preferred) or similar, with practical experience profiling, optimizing, and stabilizing non-trivial large-scale ML systemsExceptional Python engineering skills with a passion for Developer Experience (DX), elegant API design, strong typing, and composabilityExperience 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 scratchFamiliarity with analog dynamic systems, including transient responses, and nonidealities such as nonlinearity, quantization, random noise, and feedback/stabilityDemonstrated ability to reason across multiple layers of the stack: algorithm, software, compiler, runtime, and hardwareAble 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.)BenefitsBest-in-class health benefits401k matchingTruly unlimited PTOComplimentary meals when working from our Palo Alto officeCompany OverviewUnconventional AI rethinks computer foundations to optimize energy efficiency for AI. It was founded in 2025, and is headquartered in San Francisco, California, USA, with a workforce of 11-50 employees. Its website is https://unconv.ai.