Lead Machine Learning Engineer - Geometric Spatial AI (F/M/D)

OUR VISION When people use our technology to "bridge the gap" between the physical and digital worlds, they don’t just capture reality - they create a new one. In this new reality, they are smarter, more productive, more streamlined, and more creative - because they have the digital foundation to build the world they want to live in. That’s what NavVis offers in all our products and services: the tools to not just map the world as it is, but to pave the way to a better future. To forge something new. Physical or digital, there is only one reality. And it’s the reality NavVis empowers people to build better. THE OPPORTUNITY At NavVis, we build spatial intelligence technology that depends on high-quality 3D data. This is a machine learning engineering role focused on 3D geometric data. We're looking for an experienced engineer to develop and deploy machine learning models that operate on point clouds and 3D reconstructions, while raising the team's expertise in applying ML and DL to 3D data and broadening how the team uses it. You'll work closely with cross-functional teams, including AI researchers, software engineers, and product designers, to build and ship models for 3D point cloud processing, filtering, surface reconstruction, and computational geometry. Most of your time goes into moving these methods from research into production, not just exploring them. If you have practical experience and a strong theoretical grounding in cutting-edge machine learning and deep learning techniques, and want to apply them to 3D geometric data, you'll have the scope to shape how NavVis approaches 3D machine learning. HOW YOU WILL MAKE AN IMPACT • Design and optimize machine learning and deep learning models for 3D point cloud data, covering filtering, denoising, registration, and surface reconstruction • Own 3D data pipelines end-to-end , from preprocessing and augmentation to model adaptation and evaluation against ground-truth geometry • Integrate 3D ML solutions into existing workflows and products by collaborating with other teams • Adapt state-of-the-art 3D deep learning methods for production use, for example point-based architectures such as PointNet++, sparse voxel or convolutional networks, graph neural networks, and implicit or neural representations • Build scalable, reproducible training pipelines for large 3D datasets • Improve model performance by analyzing results against geometric and perceptual quality metrics WHAT WILL HELP YOU SUCCEED IN THE ROLE • MS or PhD in computer vision, machine learning, computer science, or a related field, or equivalent practical experience • 5-7+ years of hands-on experience applying machine learning and deep learning to point cloud processing, filtering, registration, or surface reconstruction, with a strong theoretical grounding in the underlying methods • Familiarity with point cloud-specific deep learning architectures, for example PointNet++, sparse convolutional networks, graph neural networks, or transformer- and diffusion-based 3D models • Strong Python skills for writing efficient, maintainable code, and hands-on experience with libraries such as PyTorch, TensorFlow, and Scikit-learn • Solid foundation in the mathematics of geometry, linear algebra, and optimization • Fluent English communication skills, needed to collaborate with AI researchers, software engineers, and product designers Would be an added benefit • Experience with computer graphics and GPU programming (CUDA, ROCm, or OpenCL) • Experience with neural 3D representations such as NeRF, Gaussian splatting, or signed distance functions • Familiarity with ML experimentation and orchestration tooling, for example MLflow, Ray, Databricks, Slurm, or Weights and Biases • Published research papers or open-so