Original listing text, shown exactly as published by the company.
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.
