Lead AI Engineer for Aerospace Design

Job DescriptionAs 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 engines, wheels and brakes, 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 DescriptionAs 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 engines, wheels and brakes, 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 research 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 's leadership in AI-enabled engineering design.You will report directly to the manager of the AI Research Group and work from our Phoenix, AZ location on a hybrid schedule.ResponsibilitiesKey ResponsibilitiesLead 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.QualificationsUS PERSON REQUIREMENTDue 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.You Must HaveBachelor'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 AnalysisExperience 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 learningWe ValueProficiency in Python and machine learning frameworks such as PyTorch and TensorFlow.Experience working in structured machine learning deployment environments i.e. MLOps workflowsDeep 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.#J-18808-Ljbffr