ML Engineer

Mach9 is seeking an ML Engineer to build perception models for AI-enabled CAD, focusing on extracting 3D features from LiDAR point clouds and imagery. The role combines research and product development, requiring end-to-end ownership from prototype to production. Ideal for early-to-mid-career engineers who thrive on shipping ML-powered features.

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

• Master's or PhD in Machine Learning, Computer Vision, Computer Science, or 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.
• Working knowledge of geometric concepts relevant to 3D perception (coordinate systems, 3D transforms).
• Strong communication skills and ability to collaborate with researchers, engineers, and product stakeholders.
• Proficient with Python and a production-quality ML library like PyTorch, JAX, or TensorFlow.

• Experience with common 3D deep learning architectures (e.g., PTv3, sparse convolutions, 3D detection/segmentation networks).
• Experience with large unstructured datasets – imagery and 3D point clouds – at scale.
• Experience delivering production-grade models with optimization techniques (quantization, pruning, distillation, runtime acceleration).
• 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.