System Modeling Engineer: GPU-Accelerated Simulations

Unconventional, Inc.; United States; Remote; USD 180,000 - 260,000; Full time

Benefits offered by this job

Health benefits

401k matching

Unlimited PTO

Complimentary meals

Job summary

Unconventional, Inc. in the United States is seeking a Member of Technical Staff, System Modeling (Dynamic Systems Simulation) to help build fast, accurate simulators that bridge SPICE-level physics with AI framework abstractions.

You will contribute to physics-based system models, GPU-accelerated simulations, and cross-layer integration. The role emphasizes deep modeling across analog/digital domains with strong ML tooling in PyTorch or JAX.

Qualifications

• MS/PhD in Electrical Engineering, Computer Engineering, or closely related fields, or BS with substantial equivalent depth.
• Dynamical systems simulation knowledge and analog/mixed-signal circuit design understanding.
• Advanced neural modeling using PyTorch or JAX with numerical solvers (e.g., Neural ODEs).
• Strong grasp of linear/non-linear dynamics, state-space, and dx/dt = f(x,u,t).
• Experience modeling noise, stochastic processes, and filtering in real systems.
• Proven experience building high-fidelity circuit simulations balancing speed and accuracy.
• Familiarity with systems engineering across analog/digital domains.
• Solid ML/AI understanding, training/inference workflows, and PyTorch proficiency.

Responsibilities

• Develop high-performance PyTorch or JAX components modeling dynamic, circuit-based systems.
• Enable next-generation AI architectures via cross-layer system integration and physics-based models.

Skills

Dynamical systems

Analog design

Neural modeling

Differential equations

Stochastic processes

Circuit simulations

Systems engineering

ML concepts

ML training

Education

MS/PhD in Electrical/Computer Engineering or related fields

Tools

CUDA

Triton

PyTorch

JAX

NumPy

Job description

Unconventional, Inc. in the United States is seeking a Member of Technical Staff, System Modeling (Dynamic Systems Simulation) to help build fast, accurate simulators that bridge SPICE-level physics with AI framework abstractions.

You will contribute to physics-based system models, GPU-accelerated simulations, and cross-layer integration. The role emphasizes deep modeling across analog/digital domains with strong ML tooling in PyTorch or JAX.