Aerospace AI Engineer: Physics-Informed Surrogates & Multi-Physics

Honeywell Aerospace US LLC; Tempe (AZ); On-site; USD 140,000 - 210,000; Full time

Job summary

Honeywell Aerospace US LLC in Tempe, AZ seeks an Advanced AI Engineer to advance AI-driven design tools and physics-based ML models. You will build surrogate models, PINNs, and digital twins, accelerating CFD, FEA, and multi-physics simulations while integrating with existing engineering workflows.

You will collaborate across global teams and research partners, contributing from concept through integration, mentoring others, and pursuing advanced AI methods for aerospace systems.

Qualifications

• Bachelor’s degree in scientific/engineering disciplines.
• 2 years of AI models for physics-based simulation experience.
• 3 years with Abaqus/Ansys scripting or similar tools.
• 5 years in CFD/structural analysis design and simulation.
• Mentoring experience.
• Experience with NVIDIA physics tools for physics-informed DL.

Responsibilities

• Develop Physics-AI and surrogate models for CFD, thermal and structural analyses.
• Create scripted FEA/CFD models to generate training data.
• Shape AI strategy for engineering design with PINNs and digital twins.
• Research multi-physics AI methods for aerospace components.
• Collaborate to integrate surrogates into design workflows.
• Support proposals and cross-functional outreach.

Skills

Python

PyTorch

TensorFlow

JAX

MLOps

Physics-informed ML

Surrogate modeling

PINNs

CFD integration

Education

Bachelor's degree

Master's preferred

Tools

Abaqus scripting

Ansys PyAnsys

APDL

NVIDIA Physics NEMO

Modulus

Warp

Job description

Honeywell Aerospace US LLC in Tempe, AZ seeks an Advanced AI Engineer to advance AI-driven design tools and physics-based ML models. You will build surrogate models, PINNs, and digital twins, accelerating CFD, FEA, and multi-physics simulations while integrating with existing engineering workflows.

You will collaborate across global teams and research partners, contributing from concept through integration, mentoring others, and pursuing advanced AI methods for aerospace systems.