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.
Our salary range is generous and we consider each individual's background and experience when determining final compensation. Base pay may vary based on role scope, job-related knowledge, skills, experience, and the Irvine, California market.
