AI-LM Engineer

We are looking for an AI-LM (Artificial Intelligence / Machine Learning) Engineer with 2 to 4 years of experience specializing in Physics AI. In this role, you will design and deploy Physics-Informed Neural Networks (PINNs), neural operators, and surrogate models that simulate complex structural, thermal, or fluid dynamics in real-time, bridging traditional numerical solvers with deep learning models.

Key Technologies

Physics-Informed Neural Networks (PINNs)

Neural Operators (FNO, DeepONet)

Partial Differential Equations (PDEs / ODEs)

PyTorch, JAX, or TensorFlow

Scientific Computing (SciPy, NumPy, DeepXDE)

Finite Element Analysis (FEA) / CFD basics

Model Optimization & Edge Deployment

Python & Numerical Simulation

What you will do

• Embed physical conservation laws (such as mass, energy, and momentum) directly into deep learning architectures and loss formulations.
• Accelerate structural, fluid, and thermal simulations by building high-fidelity neural surrogate models executing in milliseconds.
• Deploy lightweight and optimized machine learning models onto edge hardware controllers and real-time environments.
• Collaborate with mechanical, industrial, and software engineering teams to model system physics and validate model consistency.

Requirements

• 2-4 years of professional experience building and deploying machine learning models, with a strong portfolio in Physics AI or scientific computing.
• Deep mathematical grounding in partial differential equations (PDEs), calculus, and scientific simulation principles.
• Mastery of Python and core deep learning frameworks (primarily PyTorch or JAX).
• Hands-on familiarity with PINN solvers and libraries (e.g. DeepXDE, NVIDIA Modulus) is highly valued.
• M.S. or Ph.D. in Physics, Mechanical Engineering, Applied Mathematics, Computer Science, or a related quantitative discipline.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.