ML Engineer, Surrogate Modeling (Vehicle Engineering)

What you'll do

• Develop and deploy production-grade AI surrogate models to accelerate engineering simulations.
• Design and implement state-of-the-art neural architectures for complex engineering problems.
• Build scalable data pipelines to manage high-fidelity simulation results efficiently.
• Stay updated with the latest research in physics-informed ML and apply new techniques.
• Identify areas where AI can deliver significant improvements in engineering workflows.
• Develop uncertainty quantification techniques for reliable surrogate model solutions.

What we're looking for

• 1+ years of software development experience in Python for machine learning applications.
• Bachelor’s degree in computer science, data science, engineering, math, physics, or a related field; OR equivalent professional experience.
• Expert-level understanding of modern surrogate model architectures like Fourier Neural Operators (FNO) and neural operators.
• Experience training, tuning, and deploying production-grade ML models for real-world engineering problems.
• Strong background in traditional simulation methods such as CFD, FEA, thermal analysis, and their integration with AI.
• Proficiency with deep learning frameworks including PyTorch, TensorFlow, or JAX.