System Modeling; Simulation

Since 2022, AI has entered the mainstream, reshaping entire industries from education and software development to fundamental consumer behaviors. This revolution has created an unprecedented demand for computation – a demand that is now fundamentally limited by energy, not just in the datacenter, but at a global scale.

At Unconventional, our mission is to solve this. We are rethinking computing from the ground up to build a new foundation for AI that is 1000x more efficient. We’re doing this by exploiting the rich physics of semiconductors, mapping neural networks directly to the device physics rather than relying on layers of inefficient abstraction.

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 bridge the gap between SPICE‑level physical reality and AI framework‑level algorithmic abstraction.

You will be part of a hands‑on R&D team building custom, high‑speed and, high‑fidelity behavioral simulators that enable rapid iteration across all layers of unconventional physics‑based computing systems. “Extreme co‑design” is our guiding principle. We need tools that simulate complex analog and mixed‑signal systems millions of times faster than traditional circuit simulators, without losing the critical dynamics that govern system behavior.

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, and 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 or JAX components that model complex, time‑varying circuit‑based 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.

• Education
  • MS/PhD in Electrical Engineering, Computer Engineering, or closely related fields (e.g., Applied Physics with a specific focus on solid‑state devices or VLSI), or BS with substantial evidence of equivalent research/engineering depth in circuit simulation.
• Dynamical systems simulation knowledge
  • Knowledge of Analog and Mixed‑Signal circuit design: understand transistor level circuit design principles and modeling of nonidealities such as noise, mismatch, and process variations.
  • Advanced Neural Modeling (PyTorch or JAX): proficiency in PyTorch or JAX, 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: proven industry experience building high‑fidelity circuit 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.

• Software engineering
  • 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…