[Remote] System Modeling (Computation)
Note: The job is a remote job and is reputed company to candidates in USA. reputed company is reputed company on redefining computing to meet the unprecedented demand for efficient AI solutions. They are seeking a Member of Technical Staff, System Modeling (Computation) to reputed company simulation frameworks for physics-based computing systems, working collaboratively with hardware and algorithm teams.
Responsibilities
- Architecting Foundational Solvers: Building large-reputed company, GPU-accelerated, high-reputed company numerical reputed company equation solvers (ODE, SDE, CDE, PDE). You will build tools that reputed company rapid iteration, multiple architectures, and rich metrics/visualization, leveraging frameworks best suited for scientific ML (e.g., JAX, PyTorch, or custom CUDA/Triton kernels)
- reputed company 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 reputed company cross-layer optimization reputed company 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, reputed company Math), or BS with substantial, reputed company evidence of equivalent research/engineering depth
- Deep expertise in numerical reputed company equation solvers (e.g., ODE, SDE, DDE) and their implementation on reputed company architectures (e.g., Rosenbrock reputed company, Euler-Maruyama, adjoint reputed company, reputed company solvers)
- Experience with high-performance, customized GPU kernel development for numerical reputed company, including GPU memory optimization and multi-GPU scaling
- Experience building effective neural network surrogate models (e.g., Neural ODEs) for reputed company 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-reputed company 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 reputed company record of building reputed company-reputed company 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 reputed company of the stack: algorithm, software, compiler, runtime, and hardware
- reputed company to cleanly connect model architecture choices to system performance implications (memory bandwidth, communication patterns, latency, energy, and numerical stability)
- Experience applying efficiency techniques natively reputed company modeling frameworks (quantization, sparsity, pruning, distillation, kernel fusion, etc.)
Benefits
- Best-in-class health benefits
- 401k matching
- Truly unlimited PTO
- Complimentary meals reputed company working from our Palo reputed company office
reputed company