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Tier-1 brand, mid-level role, and metro location but niche scientific ML reduces applicant density.
Requires domain-specific scientific ML, PDEs, and simulation experience, limiting cross-industry fit.
Explicit 5+ years, Master/PhD and mandatory PINN/PDE/GPU skills enforce strict filters.
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Design, train, and validate Physics-Informed Neural Networks and neural operator models to accelerate or replace traditional simulation methods.
Develop scalable surrogate models and real-time simulation tools optimized for multi-GPU environments impacting automotive product development.
Collaborate with simulation engineers and cross-functional teams to translate scientific ML research into production-ready automotive engineering solutions, including digital twins.
Master’s or PhD in Mechanical Engineering, Computer Science, Applied Mathematics, or related field.
5+ years of experience in scientific machine learning, computational engineering, or related domain.
Proficiency with PINN frameworks (e.g., DeepXDE, NVIDIA Physics NeMo) and Python with PyTorch or TensorFlow.
Experience with GPU computing, distributed training, and strong understanding of partial differential equations and numerical methods.
Experienced in integrating machine learning models directly into engineering simulation workflows, especially in automotive domains like CFD and structural analysis.
Skilled in scalable model development and optimization for GPU-accelerated, real-time computational frameworks.
Able to collaborate effectively across simulation engineering and product teams to deliver production-grade tooling impacting product development.