Lead AI Engineer

Job Description
As a Lead AI Engineer at Honeywell Aerospace, you will provide expert-level technical leadership in the development of AI-driven engineering design tools and physics-based machine learning models. This role focuses on applying artificial intelligence, physics-informed methods, and surrogate modeling to accelerate the design and analysis of aerospace mechanical systems, including wheels and brakes, fuel systems, environmental control systems, and other complex components.

You will help shape the next generation of simulation and analysis capabilities by integrating AI with traditional computational tools such as CFD, FEA, and multi-physics solvers.

Job Description
As a Lead AI Engineer at Honeywell Aerospace, you will provide expert-level technical leadership in the development of AI-driven engineering design tools and physics-based machine learning models. This role focuses on applying artificial intelligence, physics-informed methods, and surrogate modeling to accelerate the design and analysis of aerospace mechanical systems, including wheels and brakes, fuel systems, environmental control systems, and other complex components.

You will help shape the next generation of simulation and analysis capabilities by integrating AI with traditional computational tools such as CFD, FEA, and multi-physics solvers.
In this role, you will craft and lead multi-year AI strategies, develop advanced AI surrogates and Physics-AI models, and transition these technologies into engineering workflows across Honeywell Aerospace. You will collaborate with cross-functional teams and global research partners, pursue both internal and government research funding, and guide execution from concept through integration. Your work will significantly impact engineering efficiency, product performance, and Honeywell Aerospace leadership in AI-enabled engineering design.
You will report directly to the manager of the Analysis and Mechanical Design Engineering team and work from our South Bend, IN location on a hybrid schedule.
Responsibilities


KEY 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.

• Bachelor’s degree from an accredited institution in a technical discipline such as science, technology, engineering, mathematics
• 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

• Bachelor’s or Master’s degree in aerospace engineering, mechanical engineering, or a related engineering discipline.

• Experience working in structured machine learning deployment environments i.e. MLOps workflows
• Deep knowledge of CFD, structural analysis,…