Machine Learning Engineer

Optimal Inc. is seeking a Machine Learning Research Scientist in Warren, MI to design and deploy AI models that merge physical principles with multimodal data. The role emphasizes Physics-Informed Neural Networks (PINNs) and predictive world models for simulating complex systems and forecasting real-world behavior.

Focus Areas

• Physics-Informed Neural Networks (PINNs)
• Predictive world models for learning system dynamics
• Physics-informed machine learning approaches for simulation and forecasting

Responsibilities

• Develop and implement Physics-Informed Neural Networks (PINNs) and other physics-informed machine learning models.
• Build multimodal models that integrate inputs such as images, sensor measurements, time-series, and simulation outputs.
• Create predictive world models to learn system dynamics, simulate environments, and forecast future states.
• Integrate physics-based simulations with data-driven machine learning methods.
• Validate 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 with multimodal modeling and/or world model development.
• Strong Python skills, with experience in PyTorch or TensorFlow.
• Knowledge of neural networks, physical systems, numerical methods, or simulation.

Technical Skills

• Python
• PyTorch
• TensorFlow

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.

Location

Warren, MI (onsite)