Physics-AI Engineer: Surrogate Modeling for Fast FEA

Cadence; United States; On-site; USD 120,000 - 160,000; Full time

Benefits offered by this job

Health insurance

On-site gym

Free meals

Company transport

Pension plan

Job summary

Cadence is seeking an AI Engineer to join our simulation team, bridging mechanical engineering and AI to advance AI-enabled CAE pipelines. You will design, train, and deploy AI models that accelerate finite element analyses while maintaining physical accuracy.

You will collaborate with CAE platforms such as NASTRAN, Abaqus, and ANSYS, using Python and deep learning frameworks like PyTorch or TensorFlow, and you will contribute to production deployment with C++ and software interfaces.

Qualifications

• Bachelor, Master, or PhD in Mechanical or Civil Engineering or related field.
• Experience with CAE/FEA software (NASTRAN, Abaqus, ANSYS).
• Solid understanding of continuum mechanics and numerical methods.
• AI/ML proficiency with Python and frameworks like PyTorch or TensorFlow.
• Familiarity with generative modeling in physical sciences.
• Software engineering skills in C++ and deploying models into production.
• Proficiency in English.

Responsibilities

• AI surrogate modeling to develop reduced-order models and deep learning surrogates for FE analyses.
• Study stamping physics and composite materials to predict manufacturability and key results.
• Design AI models to predict structural outcomes with physical consistency.
• Integrate and deploy ML models into engineering workflows with APIs and interfaces.
• Generate synthetic data using existing simulation frameworks and label datasets.
• Build validation pipelines testing accuracy, latency, and failure modes versus traditional solvers.

Skills

Python & ML frameworks

C++ programming

English proficiency

Education

Bachelor/Master/PhD in Mechanical or Civil Engineering or related field

Tools

NASTRAN

Abaqus

ANSYS

PyTorch

TensorFlow

Job description

Cadence is seeking an AI Engineer to join our simulation team, bridging mechanical engineering and AI to advance AI-enabled CAE pipelines. You will design, train, and deploy AI models that accelerate finite element analyses while maintaining physical accuracy.

You will collaborate with CAE platforms such as NASTRAN, Abaqus, and ANSYS, using Python and deep learning frameworks like PyTorch or TensorFlow, and you will contribute to production deployment with C++ and software interfaces.