Role overview
We are seeking a Machine Learning / Computer Vision Engineer to join our team. You will work on advancing our machine learning capabilities across the full pipeline — from data processing to model development to reporting. This is a hands-on role requiring both research awareness and production-minded engineering.
What you'll work on
- Design, train, and evaluate classification models for complex visual and geometric data
- Implement and benchmark modern vision foundation models (DINOv2, CLIP, ViT, ConvNeXt, or similar)
- Build learned multi-view fusion architectures (e.g., MVCNN) for combining information across multiple perspectives of an object
- Fine-tune pre-trained vision backbones on domain-specific imagery
- Develop multimodal models that combine visual features with structured text and attribute data
- Explore 3D geometry-based classification using point cloud methods (PointNet++, Point Transformer, DGCNN, or similar)
- Evaluate model performance through rigorous metrics, ablation studies, and iterative experimentation
- Contribute to data pipeline development, automated reporting, and system productionization
What we're looking for
- 3D Point Cloud Learning — PointNet, PointNet++, DGCNN, or Point Transformer
- Multimodal ML — combining vision and text/structured data (cross-attention, fusion architectures)
- 3D data formats & tools — STEP, IGES, B-Rep; Open3D, trimesh, FreeCAD, Creo Parametric or SolidWorks
- CAD-native learning — awareness of UV-Net, BRepNet, or DeepCAD
- MLOps — experiment tracking (MLflow, W&B), model versioning, CI/CD
- Manufacturing/engineering domain knowledge — part taxonomies, attribute systems
- Experience productionizing research-stage ML code (packaging, testing, configuration, logging)
- GPU/CUDA environment setup and management
Tags & focus areas
Used for matching and alerts on DevFound Fulltime Machine Learning Computer Vision Ai