3D Machine Learning Engineer

What You’ll Do

• Design and implement scalable machine learning pipelines for large-scale 3D spatial data processing for point cloud analysis, object detection, segmentation, and scene understanding.

• Train, optimize, and deploy deep learning models using PyTorch, TensorFlow, or equivalent frameworks on cloud platforms such as AWS (e.g., SageMaker, EC2).

• Collaborate with software and systems engineers to integrate models into production environments and continuously improve inference pipelines.

• Analyze diverse sensor inputs, including RGBD imagery, LiDAR point clouds, 360 photos, audio, and Building Information Models (BIM).

• Work closely with the labeling and data operations teams to define robust data annotation strategies and ensure high model performance and generalization.

What You Have

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Robotics, or a related technical field.

• 2+ years of hands-on industry experience developing and deploying machine learning systems for 3D point clouds, perception, or spatial understanding tasks.

• Strong background in 3D machine learning, with experience in deep learning for point clouds, multi-view fusion, or geometric learning.

• Strong expertise in Python and deep learning frameworks: PyTorch, TensorFlow, or similar.

• Familiarity with OpenCV and PCL (Point Cloud Library) for classical computer vision and 3D data preprocessing.

• Experience training, evaluating, and deploying ML models using cloud infrastructure (e.g., AWS, SageMaker) and containerized workflows.

• Solid understanding of the end-to-end ML lifecycle, including experiment tracking, reproducibility, model versioning, and optimization for production.

• Proven ability to work in fast-paced, interdisciplinary teams across software, ML, and product teams.

The Extras That Set You Apart

• Experience working with BIM data, digital twins, or construction-related sensor data.

• Background in geometric deep learning, 3D mesh analysis, GIS systems, or structured scene representations.

• Familiar with MLOps pipelines using Ray, SageMaker, MLflow, or Kubeflow.

• Strong foundation in geometric computer vision, robotics, or algorithmic 3D reasoning.

• Exposure to graph neural networks, geodesic computations, or neural implicit representations (e.g., NeRF, Occupancy Networks).

• Deep experience with point cloud and graph learning frameworks such as Open3D-ML, Torch-Points3D, PyG, or MMDetection3D.

• Experience building custom modules for SparseConvNet or 3D transformers.