Machine Learning Engineer
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.