AI-Developer (gn) CAD-Tools

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For our renowned client, an established company in the energy technology sector, we are looking for you as a AI-Developer (gn) CAD-Tools

• Permanent position with option to be hired by the partner company
• Compensation according to the GVP collective agreement with the option of above-tariff allowances, vacation, Christmas bonus, company pension scheme 
• "Employee referral bonus" up to 400 € 
• Vacation requests / time tracking via app, employee discounts for online shops 

• Design and implement a parametric and implicit geometry programming framework for turbomachinery components (e.g., turbine blades, vanes), enabling systematic feature variation and high-fidelity shape control
• Build and curate large-scale geometry datasets (including metadata, schemas, and versioning) to train and evaluate an AI foundation model for geometry; ensure data quality, consistency, and IP compliance
• Develop methods to represent geometry for AI (e.g., CAD kernels, constructive solid geometry, signed-distance/field-based models, meshes/point clouds) and evaluate model performance and generalization
• Integrate and automate multi-physics simulation chains (CFD/CHT/FEA) to generate physics-based labels and performance metrics for training and validation, including robust meshing and model setup
• Create surrogate and reduced-order models for thermal and structural performance; link them to design optimization workflows and digital twin concepts
• Implement scalable submission of large simulation batches to HPC/grid environments; optimize pipelines for reliability, throughput, and cost (schedulers, containers, job arrays, CI/CD)
• Collaborate with CAD/CAE, cooling design, and AI teams to capture requirements, validate models, and ensure usability and accuracy of tools in production design environments
• Establish documentation, coding standards, and training materials; mentor users and drive adoption across engineering teams

• PhD in Mechanical Engineering, Aerospace Engineering, Computer Science, or a closely related field, with emphasis on geometry processing, CAD/CAE, or thermo-fluids
• Solid understanding of internal cooling for turbine blades (e.g., serpentine channels, impingement, pin-fin arrays, film cooling) and the basics of CHT in turbomachinery
• Strong programming skills: Python and C/C++; proficiency with Linux and GitLab; experience with API-driven CAD/CAE automation (e.g., NX Open, Star-CCM+ macros)
• Experience with AI/ML applied to geometry and/or physics (e.g., mesh/graph neural networks, diffusion models, surrogate modeling), including dataset design, training, and validation at scale
• Familiarity with CFD, CHT, and FEA workflows; ability to set up, automate, and validate simulations for thermal and structural performance