AI for CAE Engineer / Senior Engineer

AI for CAE Engineer / Senior Engineer
Focus Area: Surrogate Modeling, Reduced-Order Modeling, and AI-Accelerated Simulation
Location: Remote within EU
Department: R&D / CAE Core Technology
About the Role
We are looking for an AI for CAE Engineer / Senior Engineer to help build the next generation of intelligent simulation capabilities for industrial software.
This role will focus on applying machine learning and scientific AI methods to accelerate engineering simulation workflows, with particular interest in surrogate modeling, reduced-order modeling, and data-driven approximation of high-fidelity CAE results. The work will be closely connected to production-grade CAE software and solver workflows, rather than pure academic research.
The ideal candidate combines strong fundamentals in mechanics, numerical simulation, or CAE with hands-on experience in machine learning for physical systems, and is excited by the challenge of turning advanced algorithms into robust industrial software capabilities.
Key Responsibilities

• Design and develop surrogate models for CAE applications to reduce simulation turnaround time while maintaining engineering accuracy.
• Build reduced-order or data-driven models for selected simulation workflows, including scenarios related to manufacturing and forming simulation.
• Explore and implement AI methods for simulation acceleration, fast design iteration, parameter exploration, and early-stage engineering decision support.
• Work with solver, pre/post-processing, and application teams to define where AI can create measurable value inside the CAE workflow.
• Develop model training, validation, and benchmarking pipelines using simulation-generated datasets.
• Improve model robustness, physical consistency, interpretability, and generalization across varying geometries, boundary conditions, and process parameters.
• Investigate approaches such as physics-informed learning, operator learning, graph-based learning, or hybrid physics-ML methods where appropriate.
• Support integration of AI models into internal tools or productized software workflows.
• Document technical methods, validation results, limitations, and deployment recommendations clearly for both R&D and product teams.

Candidate Profile

• Master’s or PhD degree in Mechanical Engineering, Applied Mathematics, Computational Mechanics, Computer Science, Physics, or a related field.
• Strong understanding of CAE, numerical simulation, or computational engineering.
• Hands-on experience in one or more of the following:
  • surrogate modeling
  • reduced-order modeling
  • physics-informed machine learning
  • operator learning
  • scientific machine learning
  • AI methods for engineering simulation
• Solid programming skills in Python and good software engineering habits.
• Experience with at least one ML framework such as PyTorch or TensorFlow.
• Good understanding of data processing, model evaluation, and numerical robustness.

• Ability to work across algorithm, engineering, and product teams.

Preferred Experience

• Experience with finite element methods, nonlinear mechanics, material modeling, contact, or manufacturing simulation.
• Familiarity with sheet metal forming, springback prediction, compensation workflows, or process simulation is highly valuable.
• Experience turning research ideas into software features or internal tools.
• Exposure to C++ is a strong plus, especially in engineering software environments.
• Experience with HPC, GPU acceleration, large-scale simulation datasets, or performance-sensitive workflows is a plus.
• Familiarity with uncertainty quantification, active learning, or design optimization is beneficial.

What You Will Work On

• Fast predictive models that approximate selected high-fidelity CAE results.
• AI-assisted workflows that shorten iteration cycles for engineering teams.
• Hybrid physics + machine learning approaches that balance speed, accuracy, and reliability.
• Product-oriented simulation intelligence capabilities that can be integrated into industrial software platforms.

What We Are Looking For

• Strong technical depth with a practical engineering mindset.
• Curiosity about how AI can create real value in CAE, not just research novelty.
• Ability to connect algorithms with industrial use cases and software implementation.
• Clear communication and a collaborative working style.

Nice to Have

• Publications or project work in scientific machine learning or computational mechanics.
• Experience in automotive, manufacturing, or industrial simulation use cases.
• Familiarity with commercial or in-house CAE software environments.
• Chinese and English working proficiency.

Why This Role Is Interesting

• Opportunity to work at the intersection of CAE core technology and next-generation AI methods.
• Chance to build industrially relevant AI capabilities, not only prototypes.