CAE AI Intern

Company Description SimProof is a specialized software company focused on making engineering simulations more reliable and traceable. The product automatically reads simulation files, runs dozens of deterministic checks for common setup and physics errors, and generates reports where every number can be traced back to its source file and line. SimProof is solver-neutral and currently supports tools like Ansys Mechanical and MAPDL, with early support for Nastran, Abaqus, and CalculiX, all while running offline so models never leave the user’s machine. The solution has already identified critical issues in real commercial studies, demonstrating its impact on engineering quality. SimProof was founded by an experienced simulation engineer with industry background at Mercedes-Benz AG and Valeo.
Role Description The CAE AI Intern will work full time on-site in Stuttgart and collaborate closely with the founder to improve automated checks for engineering simulations. Day-to-day tasks include analyzing CAE models and simulation results, identifying typical setup and physics errors, and helping design rule-based and AI-assisted approaches to detect these issues. The intern will contribute to data preparation, feature extraction, and model evaluation for AI components, as well as assist in integrating these methods into the existing SimProof workflow. Additional responsibilities may include writing clear documentation, supporting pilot studies with hardware teams and consultancies, and providing feedback on usability and accuracy from an engineer’s perspective. This role offers exposure to real-world simulation problems and practical applications of AI in engineering verification.
Qualifications

• Foundational skills in computer-aided engineering (CAE), including understanding of FEA or CFD workflows, mesh, boundary conditions, and material models.
• Basic experience with common simulation tools (e.g., Ansys, Abaqus, Nastran, CalculiX, or similar) and interest in verification and validation of models.
• Programming skills useful for automation and AI, such as Python and familiarity with numerical or scientific libraries (e.g., NumPy, pandas, SciPy).
• Initial exposure to AI or machine learning concepts, including data preprocessing, feature engineering, and model evaluation, is highly beneficial.
• Ability to read and understand technical documentation, write clear reports, and communicate findings in a structured, concise way.
• Currently enrolled in or recently completed a degree in mechanical engineering, aerospace engineering, computational engineering, computer science, or a related field.
• Strong attention to detail, willingness to question simulation results, and a focus on reproducibility and traceability of data.
• Comfort working in a small, fast-moving environment, with the ability to learn independently and take ownership of tasks.
• Professional working proficiency in English; German language skills are a plus but not required.