Machine Learning Engineer

Optimal Solution Company; Warren (MI); Presencial; USD 120 000 - 180 000; Tempo integral

Resumo da oferta

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.

Qualificações

• 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.

Responsabilidades

• 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.

Conhecimentos

PINNs

Multimodal modeling

World models

Python

PyTorch

TensorFlow

Numerical methods

Physics-based simulation

Formação académica

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

Ferramentas

Python

PyTorch

TensorFlow

Descrição da oferta de emprego

Job Summary

We are seeking a Machine Learning Research Scientist with experience in Physics-Informed Neural Networks (PINNs), multimodal modeling, and world model development. The candidate will develop AI models that combine physical principles and multimodal data to simulate complex systems and predict real-world behavior.

Key Responsibilities

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

Required Qualifications

• Master's or PhD in Computer Science, Machine Learning, Applied Mathematics, Physics, Engineering, or a related field.
• 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.

Preferred Qualifications

• Research or industry experience in automotive, robotics, autonomous systems, or physical AI.
• Experience with sensor fusion, differential equations, simulation, or predictive dynamics models.
• Relevant research publications or project experience.