Physics-Informed ML Engineer: Multimodal World Models

Optimal Solution Company; Warren (MI); On-site; USD 120,000 - 180,000; Full time

Job summary

Optimal Solution Company in the United States seeks a Machine Learning Research Scientist to develop Physics-Informed Neural Networks (PINNs) and physics-informed ML models that merge theory with data.

You will build multimodal models, integrate images, sensors, time-series and simulations, create predictive world models, and validate physical consistency against real or simulated data.

Qualifications

• Master's or PhD in CS/ML/Applied Math/Physics/Engineering.
• Hands-on experience with PINNs or scientific machine learning.
• Experience in multimodal modeling and/or world model development.
• Strong Python programming skills and experience with PyTorch or TensorFlow.
• Knowledge of neural networks, physical systems, numerical methods, or simulation.

Responsibilities

• Develop and implement Physics-Informed Neural Networks (PINNs) and physics-informed ML models.
• Build multimodal models integrating images, sensor data, time-series and simulations.
• Develop predictive world models to learn system dynamics and forecast future states.
• Combine physics-based simulations with data-driven ML approaches.
• Validate model accuracy, physical consistency and performance with real or simulated data.

Skills

PINNs

Multimodal modeling

World models

Python

PyTorch

TensorFlow

Numerical methods

Physics-based simulation

Education

Master's or PhD in CS/ML/Applied Math/Physics/Engineering

Tools

Python

PyTorch

TensorFlow

Job description

Optimal Solution Company in the United States seeks a Machine Learning Research Scientist to develop Physics-Informed Neural Networks (PINNs) and physics-informed ML models that merge theory with data.

You will build multimodal models, integrate images, sensors, time-series and simulations, create predictive world models, and validate physical consistency against real or simulated data.