Skip to content
Staff Machine Learning Engineer – Lidar
| Company | AiDash |
|---|
| Location | Palo Alto, CA, USA |
|---|
| Salary | $190000 – $230000 |
|---|
| Type | Full-Time |
|---|
| Degrees | Master’s, PhD |
|---|
| Experience Level | Senior, Expert or higher |
|---|
Requirements
- 7+ years of experience in applied machine learning or computer vision, with 3+ years focused on LiDAR or 3D data
- Proven expertise in 3D deep learning (e.g., 3D CNNs, PointNet/PointNeXt, SparseConvNet, Minkowski Engine)
- Experience building and fine-tuning transformer architectures for spatial or remote sensing applications (e.g., Swin3D, PointTransformer, GeoTransformer)
- Strong coding skills in Python and deep learning libraries (PyTorch preferred)
- Familiarity with aerial LiDAR data characteristics, including waveform/point density, elevation modeling, and coordinate systems (EPSG, UTM, etc.)
- Hands-on experience with geospatial and point cloud libraries (PDAL, Open3D, PCL)
- Understanding of GPU optimization and deployment in production (CUDA, TensorRT, TorchScript)
- Master’s or PhD in Computer Science, Remote Sensing, Geomatics, or related field
Responsibilities
- Design, implement, and optimize deep learning models for aerial LiDAR data—using CNNs, Transformers, and hybrid architectures
- Build end-to-end pipelines for object detection, segmentation, and terrain modeling from geo-referenced point clouds
- Combine aerial LiDAR with other data sources (e.g., RGB imagery, DSM/DTM, hyperspectral) using cross-modal transformers or fusion models
- Implement efficient 3D spatial reasoning using voxel grids, sparse tensors, and attention-based architectures
- Drive experimentation, model benchmarking, and ablation studies to push accuracy and efficiency boundaries
- Collaborate with geospatial scientists and ML engineers to bring models to production via scalable APIs and cloud-native services
- Apply MLOps practices to manage datasets, monitor model drift, and automate retraining workflows
Preferred Qualifications
- Experience with neural implicit models (e.g., NeRF, occupancy networks) for 3D scene modeling
- Knowledge of spatiotemporal modeling or change detection from periodic LiDAR collections
- Prior work with cloud-based pipelines (AWS SageMaker, GCP Vertex AI, or Azure ML)
- Contributions to open-source geospatial/ML tools or research publications
