Lead AI Engineer

Responsibilities

• Lead development of advanced Physics-AI models and surrogate models to accelerate engineering workflows for CFD, thermal analysis, structural analysis, and system-level simulation.
• Define and drive the AI strategy for engineering design, with a focus on physics-informed neural networks (PINNs), digital twins, and high-fidelity model surrogates.
• Research, develop, and validate new AI methodologies for multi-physics modeling of aerospace components such as engines, wheels and brakes, and mechanical actuation systems.
• Utilize and advance state-of-the-art NVIDIA simulation and AI acceleration tools, including Physics NEMO and related model-based AI frameworks.
• Collaborate closely with engineering teams to integrate surrogate models into design processes, enabling faster trade studies, optimization, and predictive analysis.
• Lead technical execution across internal and government-sponsored R&D projects and contribute to proposal development.
• Mentor AI engineers and researchers, fostering excellence, innovation, and deep technical growth.

Qualifications

• You Must Have Bachelor’s or Master’s degree in aerospace engineering, mechanical engineering, or a related engineering discipline.
• Minimum 5 years of experience developing AI models for physics-based simulation, engineering analysis, multi-physics modeling, or surrogate modeling.
• Minimum of 10 years working on design and simulation of physics of engineering systems involving concepts such as Computational Fluid Dynamics or Structural Analysis.
• Experience leading technical teams and mentoring junior engineers.
• Expert level expertise with NVIDIA’s physics-accelerated AI tools such as Physics NEMO, Modulus, Warp, or similar platforms for physics-informed deep learning.
• Proficiency in Python and machine learning frameworks such as PyTorch and TensorFlow.
• Experience working in structured machine learning deployment environments i.e. MLOps workflows.
• Deep knowledge of CFD, structural analysis, thermal modeling, or multi-physics simulation, and the ability to couple these with AI-based surrogates.
• Experience deploying AI models into engineering design workflows or digital engineering ecosystems.
• Strong understanding of current research in physics-informed ML, scientific machine learning, and surrogate modeling at major conferences and journals.