ML Engineer

Machine Learning Engineer – Perception Models At Mach9, ML Engineers build the perception models at the core of our AI‑enabled CAD system. We develop and train cutting‑edge 3D scene‑understanding models that extract objects and line features from dense LiDAR point clouds and imagery to serve real surveyors and engineers in the field. This role is both research‑driven and product‑focused. You’ll design and train models that power our automated extraction pipeline—image and 3D detection and localization—and work end‑to‑end from research prototype to production feature, partnering closely with infrastructure and product teams to take ideas from a paper to deployed capabilities. This role is ideal for early‑to‑mid‑career ML engineers who thrive on end‑to‑end ownership and can move fluidly from dissecting a new architecture paper to shipping the product feature that the resulting ML model backs. 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 an 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, such as point‑cloud backbones (e.g., PT‑v3), 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. #J-18808-Ljbffr Mach9