Unlock your potential with Dassault Systèmes, a global leader in Scientific Software Engineering as an applicative data scientist in Daegu, Korea!
 
About the Role
CATIA, a pioneer in 3D CAD and generative design, is pushing the boundaries of how artificial intelligence transforms engineering creativity. We are looking for an Applicative Data Scientist who can bridge the gap between cutting-edge generative AI and the rigorous world of industrial 3D geometry.
In this role, you will not just train models – you will embed them into CATIA’s 2D and 3D applications. You will work at the intersection of deep learning, combinatorial optimization, and computational geometry, turning research breakthroughs into practical engineering experiences that empower designers to create complex, manufacturable designs that were previously impossible to achieve manually.

Key Responsibilities

• AI Model Development & Selection
  • Select, adapt, and fine-tune generative AI technologies (diffusion models, GANs, VAEs, and LLMs) to address strategic challenges in generative design and user experience.
  • Train and qualify AI models, ensuring robust performance, scalability, and strict compliance with company-wide cybersecurity policies, as well as industrial regulations and manufacturing standards.
• Geometric Algorithm Engineering
  • Implement and optimize algorithms for 3D mesh processing, computational geometry, and spatial data structures.
  • Integrate AI-generated outputs into CATIA's applications, ensuring they satisfy engineering and manufacturing requirements such as manufacturability, geometrical constraints and design robustness.
• Mathematical Optimization & Search
  • Leverage advanced knowledge in Operations Research, Combinatorial Optimization, and Meta-heuristics (e.g., Simulated Annealing, Adaptive Large Neighborhood Search, MIP) to solve complex design-space search problems.
  • Combine generative methods with optimization techniques to propose design alternatives that satisfy multiple, often conflicting, engineering constraints.
• Knowledge Integration & Documentation
  • Actively learn and incorporate complex CAD/PLM domain knowledge into AI solution design.
  • Document best practices for selecting, deploying, and integrating AI models within engineering workflows, enabling the broader team to adopt AI technologies effectively.
• Collaboration & Knowledge Sharing
  • Work effectively in a global, multi-site R&D environment.
  • Share knowledge with software engineers, product managers, and domain experts, translating complex AI concepts into actionable engineering insights.

Education

• BSc, MSc, or PhD in Data Science, Computer Science, Applied Mathematics, Computational Geometry, or a related field. A focus on 3D technologies is strongly preferred.

Technical Expertise
AI & Machine Learning

• Solid knowledge of machine learning and deep learning, with hands-on experience in Python libraries such as TensorFlow, PyTorch, or equivalents.
• Advanced understanding of generative models, including diffusion models, GANs, and VAEs – from theory to deployment.

Mathematics & Optimization

• Advanced knowledge in mathematics applied to computer science, specifically in:
  • Operations Research (OR)
  • Combinatorial Optimization
  • Meta-heuristics (Simulated Annealing, Adaptive Large Neighborhood Search, MIP solvers, etc.)
• Strong background in numerical computation and algorithm design.

Geometry & Programming

• Strong algorithmic coding skills in both Python and C++.
• Proven experience in implementing:
  • 3D mesh processing
  • Computational geometry
  • Spatial data structures (e.g., octrees, bounding volume hierarchies)
• Knowledge of object-oriented development is a must (C++ proficiency is essential).

Soft Skills

• Eagerness to learn and integrate complex CAD/PLM domain knowledge into AI solutions.
• Ability to work effectively in a global, collaborative environment.
• Willingness to share knowledge with team members and contribute to team growth.
• Professional English proficiency (written and spoken) is mandatory.
• Commitment to long-term growth, with a genuine interest in building a sustainable career within our R&D organization and contributing to the team's long-term technical vision and culture.

Preferred Qualifications (Nice-to-Have)

• Professional experience applying AI technologies to engineering, CAD/CAE or industrial software development is highly preferred.
• Familiarity with CATIA, 3DEXPERIENCE platform, or other CAD/CAE software.
• Experience with geometric kernels (e.g., CGM, Open CASCADE, ACIS) or mesh libraries (e.g., CGAL, OpenMesh, libigl).
• Knowledge of model optimization for inference (ONNX, TensorRT) and edge/cloud deployment.
• Publications in top-tier conferences (CVPR, SIGGRAPH, NeurIPS, ICLR, etc.) related to 3D generation or geometric deep learning.

 
What’s in it for you? 

• Work on real-world industrial challenges where your AI models directly impact how the world designs and manufactures products – from automotive to aerospace.
• Collaborate with a world-class team of software engineers, mathematicians, and domain experts across the globe.
• Access to cutting-edge computational resources and large-scale 3D datasets.
• Opportunity to grow into a technical lead or architect role, shaping the future of AI-driven generative design at Dassault Systèmes.
• Hybrid working model with the opportunity to work from home part of the week.