Scientific Machine Learning Engineer

Job ID

65304

Category

PD Operations and Quality

Location

Chennai, India

Work Type

Hybrid

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 models - such as Physics-Informed Neural Networks (PINNs) and neural operators - to 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 Ford’s 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

Master’s 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