System Modeling (Simulation)

Role Description

As a Member of Technical Staff, System Modeling (Dynamic Systems Simulation), you will be part of a hands-on R&D team building simulation frameworks that enable testing and rapid iteration across all layers of unconventional physics-based computing systems for machine learning workloads. “Extreme co-design” is our guiding principle.

System Modeling is a multi-disciplinary effort, and the team we’re building reflects that. The role involves:

• Development of physics-based system models
• GPU-accelerated ML system simulations
• Cross-layer system integration

You don’t need to be an expert in all of these, but you have to be very strong in at least one, and solid in the rest.

You will be responsible for developing high-performance PyTorch components that model complex, time-varying dynamic systems. Your work will directly enable next-generation AI architectures, requiring a holistic approach involving everything from high-level neural network design down to the fundamental differential equations that govern system behavior.

Qualifications

• 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.
• Dynamical systems simulation knowledge
• Advanced Neural Modeling (PyTorch):
  • Deep proficiency in PyTorch, specifically in building custom autograd functions and integrating numerical solvers (e.g., Neural ODEs) to represent dynamic processes.
• Dynamics & Differential Equations:
  • A strong theoretical and practical grasp of linear and non-linear dynamics, state-space representations, and solving $dx/dt = f(x, u, t)$ within a machine learning context.
• Stochastic Processes & Noise:
  • Understanding how to model and mitigate noise in real-world systems, including experience with stochastic differential equations (SDEs) or Bayesian filtering.
• Modeling & Simulation (M&S):
  • Proven industry experience building high-fidelity simulations that balance computational efficiency with physical accuracy.
• Systems Engineering (Analog/Digital):
  • Familiarity with hardware-level concepts like circuit dynamics, signal processing, or transfer functions is highly desirable to help ground our digital models in physical reality.
• ML and systems fluency:
  • 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.

Requirements

• Strong Python engineering skills: modular design, testing, packaging, CI.
• Experience with PyTorch internals: autograd, custom modules, low-level ops; familiarity with torch.compile or similar graph capture/compile flows.
• Experience with CUDA, Triton, or other GPU programming approaches (writing custom kernels, understanding memory hierarchy, basic performance tuning).
• Comfort with at least some of: JAX, NumPy, TensorFlow, Modal, HPC patterns (MPI, NCCL, distributed training), SciPy.
• Demonstrated ability to reason across multiple layers of the stack: algorithm, software, runtime, hardware.
• Able to connect model architecture choices to system performance implications: memory bandwidth, communication patterns, latency, energy, and numerical issues.
• Experience applying at least some efficiency techniques (quantization, sparsity, pruning, distillation, kernel fusion, etc.).

Benefits

• A comprehensive package including best-in-class health benefits
• 401k matching
• Truly unlimited PTO
• Complimentary meals when working from our Palo Alto office