Richemont R&D Internship — AI for 3D Design Optimization

Richemont is a Swiss luxury goods holding that houses a portfolio of distinguished maisons across jewellery, watchmaking, leather goods and writing instruments. The group combines centuries-old craftsmanship with contemporary innovation and invests in technical research to support product excellence and creativity across its brands.

1. Research and evaluate state-of-the-art machine learning methods for 3D geometry, meshes, point clouds and parametric CAD data to accelerate and optimise design workflows.
2. Develop, train and validate deep learning prototypes (e.g., neural networks for mesh processing, generative design or topology optimisation) using company and public 3D datasets.
3. Produce end-to-end proof-of-concept pipelines that connect ML models to 3D tools and CAD workflows for rapid iteration by designers and model makers.
4. Curate, clean and label 3D datasets; implement data augmentation and benchmarking procedures to ensure reproducible experimental results.
5. Collaborate closely with cross-disciplinary teams — designers, prototypists, materials specialists and software engineers — to translate technical solutions into practical design aids.
6. Document methods, produce reproducible code and present findings to R&D stakeholders and brand teams; contribute to internal knowledge transfer and technical reports.

1. Enrolment in a Master’s or engineering programme (Computer Science, Mechanical Engineering, Computational Design, Data Science or equivalent) with strong emphasis on machine learning or computer graphics.
2. Solid programming proficiency in Python and experience with ML frameworks (PyTorch or TensorFlow).
3. Hands-on experience with 3D data (mesh and point-cloud processing, CAD formats) and associated tooling.
4. Strong mathematical foundations in linear algebra, geometry processing and optimisation techniques.
5. Ability to communicate technical results clearly to non-technical stakeholders and to work within multidisciplinary teams.

1. Python
2. PyTorch
3. TensorFlow
4. Open3D
5. Blender
6. Rhino/Grasshopper
7. SolidWorks
8. CAD
9. Git
10. CUDA
11. NumPy
12. SciPy
13. scikit-learn
14. OpenCV

Practical experience from academic projects, research internships or personal projects applying machine learning to 3D geometry, computational design or generative modelling. Demonstrable code repositories, project reports or publications are advantageous.

Currently enrolled in a Master’s or equivalent engineering degree with coursework or specialisation in machine learning, computer vision, computational geometry or computational design.

Richemont balances artisanal heritage with technical innovation, fostering a collaborative environment where designers, engineers and researchers converge. The organisation values precision, discretion and long-term investment in skills and technology, encouraging cross-disciplinary exchange and applied research that enhances product excellence.