Job ist nicht gleich Job. Er soll zu dir passen, dich fordern und angemessen bezahlt sein. Das sehen wir auch so, deswegen bringt PAMEC dich mit den Unternehmen zusammen, die wirklich zu dir passen. Wenn du deinen Job liebst, machen wir unseren richtig – und das seit 1984.
For our renowned client, an established company in the energy technology sector, we are looking for you as a AI-Developer (gn) CAD-Tools
These are your advantages
1. Permanent position with option to be hired by the partner company
2. Compensation according to the GVP collective agreement with the option of above-tariff allowances, vacation, Christmas bonus, company pension scheme
3. "Employee referral bonus" up to 400 €
4. Vacation requests / time tracking via app, employee discounts for online shops
This is what your daily work routine looks like
5. 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
6. 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
7. 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
8. 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
9. Create surrogate and reduced-order models for thermal and structural performance; link them to design optimization workflows and digital twin concepts
10. Implement scalable submission of large simulation batches to HPC/grid environments; optimize pipelines for reliability, throughput, and cost (schedulers, containers, job arrays, CI/CD)
11. 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
12. Establish documentation, coding standards, and training materials; mentor users and drive adoption across engineering teams
What makes you suitable for the job
13. PhD in Mechanical Engineering, Aerospace Engineering, Computer Science, or a closely related field, with emphasis on geometry processing, CAD/CAE, or thermo-fluids
14. 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
15. 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)
16. 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
17. Familiarity with CFD, CHT, and FEA workflows; ability to set up, automate, and validate simulations for thermal and structural performance
Job ist nicht gleich Job. Er soll zu dir passen, dich fordern und angemessen bezahlt sein. Das sehen wir auch so, deswegen bringt PAMEC dich mit den Unternehmen zusammen, die wirklich zu dir passen. Wenn du deinen Job liebst, machen wir unseren richtig – und das seit 1984.
For our renowned client, an established company in the energy technology sector, we are looking for you as a AI-Developer (gn) CAD-Tools
These are your advantages
18. Permanent position with option to be hired by the partner company
19. Compensation according to the GVP collective agreement with the option of above-tariff allowances, vacation, Christmas bonus, company pension scheme
20. "Employee referral bonus" up to 400 €
21. Vacation requests / time tracking via app, employee discounts for online shops
So sieht Dein Arbeitsalltag aus
This is what your daily work routine looks like
22. 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
23. 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
24. 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
25. 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
26. Create surrogate and reduced-order models for thermal and structural performance; link them to design optimization workflows and digital twin concepts
27. Implement scalable submission of large simulation batches to HPC/grid environments; optimize pipelines for reliability, throughput, and cost (schedulers, containers, job arrays, CI/CD)
28. 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
29. Establish documentation, coding standards, and training materials; mentor users and drive adoption across engineering teams
Was Dich für den Job auszeichnet
What makes you suitable for the job
30. PhD in Mechanical Engineering, Aerospace Engineering, Computer Science, or a closely related field, with emphasis on geometry processing, CAD/CAE, or thermo-fluids
31. 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
32. 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)
33. 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
34. Familiarity with CFD, CHT, and FEA workflows; ability to set up, automate, and validate simulations for thermal and structural performance
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