Lead AI Engr at Honeywell Aerospace

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

RESPONSIBILITIES

Lead AI Engineer – Mechanical Design, CFD & Structural Analysis

We are looking for a Lead AI Engineer with strong expertise in mechanical

design, computational fluid dynamics, and structural analysis to join the

Aerospace Advanced & Applied Technology group. In this role, you will develop

and deploy AI-enabled engineering solutions that accelerate simulation

workflows, improve design exploration, enhance prediction accuracy, and support

technology insertion for aircraft propulsion engines, power systems, and related

mechanical components. You will work with product architects, simulation

specialists, product chiefs, Chief Engineers, and cross-functional teams to

translate advanced analytics, AI/ML methods, and physics-based insights into

practical engineering solutions that create measurable business value.

Key Responsibilities:

* Lead the development and deployment of AI/ML-enabled methods for mechanical

design, CFD, structural analysis, and simulation-driven product development.

* Build surrogate models, reduced-order models, physics-informed models,

optimization frameworks, and automation workflows for engineering

applications.

* Apply AI/ML techniques to improve simulation efficiency, design exploration,

predictive capability and engineering decision-making.

* Collaborate with product architects, Chief Engineers, Subject specialists,

and cross-functional teams to identify high-value engineering problems and

convert them into scalable AI-enabled solutions.

* Interpret CFD and structural analysis results, validate model assumptions,

assess technical risks, and recommend design improvements based on data and

physics-based insights.

* Automate engineering workflows including data extraction, preprocessing,

model training, post-processing, visualization, and report generation.

* Drive adoption of advanced AI/ML engineering methods across teams through

technical leadership, mentoring, and stakeholder engagement.

* Ensure solutions are technically robust, explainable, traceable, and aligned

with aerospace engineering quality, validation, and business requirements.

QUALIFICATIONS

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,

Read the full description on the employer’s site →