Machine Learning / Computer Vision Engineer - NEED LOCALS ONLY

Role: Machine Learning / Computer Vision Engineer

Location: Morgan Hill, CA (Preferably Bay area candidates if Remote they should work in PST Time Zone)

Duration: Long Term

Job Summary :

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.

Responsibilities:

• 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

Required Skills:

• Python strong proficiency
• PyTorch model development, custom training loops, fine-tuning, inference
• Computer Vision transfer learning, feature extraction, embedding-based methods
• Vision Foundation Models hands-on experience with at least one of: DINOv2, CLIP, ViT, ConvNeXt, EfficientNet-V2
• Multi-view 3D Recognition familiarity with MVCNN or learned view-pooling techniques
• ML evaluation classification metrics, stratified data splitting, experiment design

Preferred / Nice-to-Have:

• 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

Education:

• BS/MS in Computer Science, Machine Learning, Computer Vision, Data Science, Engineering, or equivalent practical experience.