Lead AI Engineer
Indexed description
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, 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.
- Employer-subsidized medical, dental, vision and life insurance
- Short-term and long-term disability coverage
- 401(k) match, flexible spending accounts and health savings accounts
- Employee assistance program and educational assistance
- Parental leave and 12 paid holidays
- Paid time off for vacation, personal and sick time
POSTING TIMELINE
The application period for the job is estimated to be 40 days from the job posting date; however, this may be shortened or extended depending on business needs and the availability of qualified candidates.
Us Citizenship Requirements
Due to compliance with U.S. export control laws and regulations, candidate must be a U.S. Person, which is defined as, a U.S. citizen, a U.S. permanent resident, or have protected status in the U.S. under asylum or refugee status or have the ability to obtain an export authorization.
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