Scientific Machine Learning Engineer

As a Scientific Machine Learning Engineer within the Methods Team you will work at the intersection of computational science engineering simulation and artificial intelligence. You will develop advanced machine learning modelssuch as Physics-Informed Neural Networks (PINNs) and neural operatorsto augment or replace computationally expensive simulations (e.g. fluid dynamics and structural analysis).

Leveraging NVIDIA platforms (e.g. Physics NeMo) and GPU computing you will help build scalable real-time simulation tools that directly influence Fords product development. You will collaborate closely with simulation engineers and cross-functional teams to translate research innovations into production-ready solutions.

• Design train and validate Physics-Informed Neural Networks (PINNs) and neural operator models (e.g. Fourier Neural Operators DeepONet)
• Develop surrogate models to accelerate or replace traditional simulation methods
• Implement scientific machine learning workflows using NVIDIA Physics NeMo or comparable frameworks
• Apply ML methods to automotive engineering domains including:
  • Computational Fluid Dynamics (CFD)
  • Structural mechanics and crash simulation
  • Multibody dynamics
• Perform uncertainty quantification (UQ) and sensitivity analysis
• Optimise models for multi-GPU environments
• Collaborate with simulation engineers and product teams to deliver production-ready tools
• Contribute to development of digital twins and real-time simulation capabilities

Education

• Masters or PhD in Mechanical Engineering Computer Science Applied Mathematics or a related field

Experience

• 5 years in scientific machine learning computational engineering or related domain

Technical Skills

• Experience with PINN frameworks (e.g. DeepXDE NVIDIA Physics NeMo or similar)
• Strong proficiency in Python with experience in PyTorch or TensorFlow
• Understanding of partial differential equations (PDEs) and numerical methods
• Experience working with GPU computing and distributed training
• Familiarity with scientific computing workflows

Nice to Have

• Experience with C and/or CUDA
• Exposure to automotive simulation tools (CFD FEA)
• Experience applying machine learning in engineering domains
• Familiarity with Large Language Models (LLMs) applied to engineering or simulation workflows