ML Engineer

Responsibilities

• Design, train, and evaluate computer vision and 3D ML models for extracting CAD-grade geometry and features from dense LiDAR and imagery.
• Drive ML research that translates directly into product capabilities: prototyping new approaches, running experiments, and identifying what’s shippable.
• Own models through the full product lifecycle: problem framing, data strategy, training, evaluation, and final integration into our cloud-based CAD software, Digital Surveyor.
• Develop evaluation methodology and metrics that reflect real surveying and engineering accuracy requirements.
• Work with ML infrastructure engineers to scale training and inference of your models and with product teams to align your model’s behavior with what the user wants.

Requirements

• Master's or PhD in Machine Learning, Computer Vision, Computer Science, or a related field, or equivalent industry experience.
• Strong foundation in computer vision and deep learning, with hands-on experience training models for segmentation, detection, or 3D understanding.
• Experience taking a ML model from research/prototype to production, not just publishing or benchmarking.
• Working knowledge of geometric concepts relevant to 3D perception like coordinate systems and 3D transforms.
• Strong communication skills and the ability to collaborate with researchers, other engineers and product stakeholders.
• Proficient with Python and a production-quality ML library like PyTorch, JAX, or TensorFlow.

Bonus Qualifications

• Experience with common 3D deep learning architectures, like point cloud backbones such as PTv3, sparse convolutions, or 3D detection/segmentation networks.
• Experience with large unstructured datasets — imagery and 3D point clouds — at scale.
• Experience delivering production-grade models with optimization techniques such as quantization, pruning, distillation, or runtime acceleration (e.g., TensorRT, ONNX Runtime).
• Familiarity with multi-GPU training and experiment management (Weights & Biases or similar).
• Publications or strong open-source contributions in computer vision or 3D machine learning.