NavVis is seeking a Lead Machine Learning Engineer to develop and deploy machine learning models for 3D point clouds and reconstructions. This role focuses on applying ML/DL techniques to 3D geometric data and moving methods from research into production. You will collaborate with AI researchers, software engineers, and product designers to build and ship models for 3D point cloud processing, filtering, surface reconstruction, and computational geometry, with emphasis on production readiness. Responsibilities include designing and optimizing ML models for 3D data (filtering, denoising, registration, surface reconstruction); owning end-to-end 3D data pipelines from preprocessing to evaluation against ground-truth geometry; integrating 3D ML solutions into existing workflows; adapting state-of-the-art 3D DL methods for production using architectures such as PointNet++, sparse convolutions, graph neural networks, and implicit/Neural representations; building scalable training pipelines; and improving model performance using geometric and perceptual quality metrics. Requirements include MS/PhD in computer vision, ML, CS, or equivalent; 5–7+ years of hands-on experience with point cloud processing; familiarity with 3D DL architectures; strong Python skills with PyTorch, TensorFlow, and Scikit-learn; solid foundation in geometry, linear algebra, and optimization; fluent English; nice-to-have items such as CUDA/ROCm/OpenCL, NeRF, neural 3D representations, ML experimentation tooling (MLflow, Ray, Databricks, Slurm, Weights & Biases), C++, Open3D, PyTorch3D, PCL, CGAL, and Linux; p

NavVis is seeking a Lead Machine Learning Engineer to develop and deploy machine learning models for 3D point clouds and reconstructions. This role focuses on applying ML/DL techniques to 3D geometric data and moving methods from research into production. You will collaborate with AI researchers, software engineers, and product designers to build and ship models for 3D point cloud processing, filtering, surface reconstruction, and computational geometry, with emphasis on production readiness. Responsibilities include designing and optimizing ML models for 3D data (filtering, denoising, registration, surface reconstruction); owning end-to-end 3D data pipelines from preprocessing to evaluation against ground-truth geometry; integrating 3D ML solutions into existing workflows; adapting state-of-the-art 3D DL methods for production using architectures such as PointNet++, sparse convolutions, graph neural networks, and implicit/Neural representations; building scalable training pipelines; and improving model performance using geometric and perceptual quality metrics. Requirements include MS/PhD in computer vision, ML, CS, or equivalent; 5–7+ years of hands-on experience with point cloud processing; familiarity with 3D DL architectures; strong Python skills with PyTorch, TensorFlow, and Scikit-learn; solid foundation in geometry, linear algebra, and optimization; fluent English; nice-to-have items such as CUDA/ROCm/OpenCL, NeRF, neural 3D representations, ML experimentation tooling (MLflow, Ray, Databricks, Slurm, Weights & Biases), C++, Open3D, PyTorch3D, PCL, CGAL, and Linux; p