Artificial Intelligence Engineer

Principal AI Software Engineer – 3D Geometry & Multimodal Models

Location: Europe

Working Model: Remote / Flexible within Europe

Level: Senior / Principal

About the Opportunity

Our client is building a new generation of AI-enabled engineering software combining artificial intelligence, computational geometry, CAE and physics-based engineering technologies.

The company is looking for a highly experienced AI Software Engineer specialising in multimodal models for 3D geometry, CAD and mesh representations.

This is a hands-on software and algorithm development role focused on building production-grade AI technologies capable of understanding, representing and generating complex engineering geometry.

The successful candidate will work at the intersection of multimodal foundation models, geometric deep learning, CAD/CAE geometry, mesh representations and engineering software development.

Responsibilities

• Design and develop multimodal AI models capable of understanding and processing engineering geometry.
• Develop models combining different engineering data modalities including:
• CAD geometry
• B-Rep / boundary representations
• surface and volume meshes
• point clouds
• images and sketches
• text and engineering metadata
• simulation and physics data
• Develop scalable representations and embeddings for complex 3D engineering geometry.
• Develop geometry encoders and tokenisation strategies suitable for large-scale multimodal models.
• Develop transformer, graph-based, generative or foundation-model architectures for 3D engineering data.
• Develop models for geometry understanding, generation, reconstruction, modification and reasoning.
• Develop AI capabilities for mesh generation, mesh understanding and geometry-to-mesh workflows.
• Integrate AI models into production engineering software products and CAE/CAD workflows.
• Develop robust training, inference and evaluation pipelines.
• Optimise models for large engineering datasets and production deployment.
• Work closely with computational geometry, CAE, meshing and HPC software teams.
• Translate state-of-the-art AI research into reliable and scalable industrial software.
• Contribute to technical architecture and long-term product strategy for geometry-based AI technologies.

Requirements

• MSc or PhD in Computer Science, Artificial Intelligence, Machine Learning, Computational Geometry, Applied Mathematics or a related discipline.
• Strong hands-on software development experience in AI/ML.
• Excellent programming skills in Python, with strong software engineering fundamentals.
• Experience with PyTorch or another major deep learning framework.
• Strong experience in one or more of the following:
• multimodal learning
• foundation models
• transformers
• generative models
• geometric deep learning
• 3D machine learning
• graph neural networks
• representation learning
• Experience working with 3D data such as meshes, CAD geometry, B-Rep, point clouds or surface representations.
• Strong understanding of modern deep learning architectures and model training methodologies.
• Ability to translate research concepts into robust production software.

Highly Desirable

• Experience with CAD, CAE or engineering geometry.
• Knowledge of B-Rep / boundary representation, NURBS or parametric CAD.
• Experience with triangle meshes, tetrahedral meshes, surface meshes or unstructured meshes.
• Experience with geometric deep learning or graph neural networks.
• Experience developing transformer architectures for 3D or geometric data.
• Experience with multimodal foundation models involving text, images and 3D representations.
• Experience with diffusion models, autoregressive models, VQ-VAE or other generative architectures.
• Experience with neural operators, Physics AI or simulation-based machine learning.
• Experience with geometry generation or AI-driven meshing.
• Experience developing commercial engineering, CAD, CAE or scientific software.
• Strong C++ skills would be advantageous.
• Experience optimising large models for GPU inference or distributed training would be beneficial.

Relevant Technical Areas

Relevant experience may include:

• 3D foundation models
• Geometric deep learning
• Generative CAD
• Neural CAD
• Mesh generation with deep learning
• 3D representation learning
• Multimodal large models
• Vision-language models applied to engineering data
• Graph transformers
• Mesh transformers
• Point-cloud transformers
• B-Rep learning
• CAD sequence modelling
• Physics-informed or physics-aware AI

Ideal Background

We are particularly interested in candidates who combine strong AI development capabilities with an understanding of geometry, CAD, CAE, 3D data or scientific engineering applications.

The strongest candidates are likely to have developed AI technologies that operate directly on meshes, CAD models, B-Rep geometry, point clouds or other complex 3D engineering representations, rather than working exclusively with text or 2D image data.