Lead AI Engr

Join a company that's reintroducing itself to the aviation community we've helped advance for more than a century. At Honeywell Aerospace (NASDAQ: HONA), we're launching as an independent, publicly traded aerospace and defense company built on a legacy of operational excellence and mission-focused execution. 

Our new brand identity pairs that heritage with real momentum, as we build technology that helps pilots navigate with confidence, aircraft operate more efficiently, and operators stay ahead of change. With our systems on board 90% of the world's aircraft, your work here has reach that's rare to find anywhere else.

Focusing on our customers, investing in innovation, and building a culture of accountability and performance is how we're shaping what comes next.

Every horizon. Every mission. Every day.

Honeywell Aerospace products and services are found on aircraft across commercial aviation, defense and space — from engines and cockpit electronics to cabin systems, mechanical components and connectivity solutions. Our technology helps operators fly more safely, reduce fuel consumption, improve on-time performance and deliver a better experience for the people on board. 

As aviation continues to evolve, we're also developing systems to support autonomous and supersonic platforms, bringing the same focus on safety and efficiency to the next era of flight. With approximately 36,000 employees worldwide and net sales of $17.4B in 2025, Honeywell Aerospace is one of the largest dedicated aerospace companies in the world. Explore our businesses: https://www.honeywellaerospace.com

Required Qualifications:

• Master’s degree in mechanical engineering, aerospace engineering, applied mechanics, data science, artificial intelligence, or a related discipline from a reputed university.
• 12 to 20 years of relevant experience in mechanical design, CFD, structural analysis, simulation-led product development, engineering automation, or AI/ML-enabled engineering applications.
• Strong fundamentals in fluid mechanics, thermodynamics, heat transfer, turbomachinery, solid mechanics, finite element methods, numerical methods, design optimization, and engineering statistics.
• Hands-on experience with CFD and structural analysis workflows, including model setup, mesh strategy, solver execution, post-processing, validation, and interpretation of results.
• Proven experience applying AI/ML methods to mechanical engineering problems using simulation, experimental, operational, or multi-physics engineering datasets.
• Experience developing surrogate models, reduced-order models, physics-informed machine learning models, response surface models, uncertainty quantification methods, or optimization frameworks.
• Strong programming capability in Python and practical experience with AI/ML libraries such as NumPy, pandas, scikit-learn, TensorFlow, PyTorch, or equivalent platforms.
• Ability to automate engineering workflows for geometry handling, meshing, solver setup, data extraction, model training, post-processing, visualization, and reporting.
• Good understanding of gas turbine engine components, aerospace mechanical systems, propulsion and power system technologies, and simulation-driven product development practices.
• Demonstrated ability to manage multiple technical priorities, deliver development milestones, and work effectively in ambiguous engineering environments.
• Excellent communication, presentation, collaboration, problem-solving, and stakeholder management skills

Preferred Qualifications:

• Experience with commercial and open-source CFD, FEA, and mechanical design tools such as ANSYS Fluent, STAR-CCM+, Abaqus, ANSYS Mechanical, Nastran, HyperMesh, NX, Creo, CATIA, or equivalent platforms.
• Experience in aircraft propulsion systems, turbomachinery, thermal systems, power systems, or aerospace component development, testing, and certification processes.
• Familiarity with aerospace design practices, industry standards, and regulatory requirements related to engine design, structural integrity, verification, validation, and certification.
• Knowledge of machine learning methods such as regression, classification, neural networks, Gaussian processes, Bayesian optimization, variational autoencoders, graph neural networks, reinforcement learning, or generative AI for engineering applications.
• Exposure to physics-informed neural networks, neural operators, AI-assisted solver acceleration, digital twins, reduced-order models, and simulation-based design optimization.
• Experience handling large engineering datasets, including data cleaning, feature engineering, model training, validation, error analysis, traceability, and deployment of reusable AI/ML workflows.
• Familiarity with cloud or HPC environments, version control, model lifecycle management, engineering data management, and collaborative software development practices.
• Experience mentoring engineers, guiding technical problem solving, and influencing adoption of new AI-enabled engineering methods across teams.

What We Value:

• A strong ownership mindset and ability to lead complex technical initiatives from concept to deployment.
• Curiosity to adopt emerging AI/ML technologies and apply them responsibly to aerospace engineering challenges.
• Ability to communicate complex technical concepts clearly to engineering, product, and business stakeholders.
• A collaborative approach to mentoring, knowledge sharing, and building reusable engineering capabilities.
• Innovative and creative mindset
• Ability to manage multiple tasks and projects efficiently, ensuring timely delivery of development milestones.

Honeywell Aerospace never requests or accepts money from candidates at any stage of the hiring process. Any demand for payment in exchange for employment opportunities is fraudulent and should be reported immediately to local law enforcement authorities