Machine Learning Engineer

Description:

Citylogix Inc. (formerly StreetScan) is a leading data and analytics company focused on enhancing transportation infrastructure for smart cities. The company uses advanced LiDAR, 360° imaging, and AI analytics to create detailed digital maps and conduct infrastructure assessments. The company’s mission is to empower municipalities with data-driven decision-making tools for better infrastructure management.

Citylogix is looking for a Machine Learning Engineer with expertise in geospatial data and LiDAR. The ideal candidate will have hands-on experience in processing LiDAR and satellite imagery data and developing machine learning models for geospatial applications. You will be working closely with the R&D team to create solutions for geospatial analysis, object detection, and 3D mapping.

Key Responsibilities:

• Develop, train, and deploy machine learning models for processing geospatial and LiDAR data.

• Design and implement algorithms for feature extraction, object detection, classification, and segmentation of LiDAR point clouds and satellite imagery.

• Process and analyze large-scale geospatial datasets, including aerial imagery, LiDAR scans, and GIS data.

• Optimize deep learning and traditional ML models for computational efficiency and scalability.

• Collaborate with cross-functional teams to integrate ML solutions into geospatial applications.

• Work with cloud-based platforms (AWS, GCP, Azure) for scalable data processing and model deployment.

• Stay up-to-date with the latest advancements in machine learning, remote sensing, and geospatial technologies.

Qualifications & Skills:

• Bachelor’s, Master’s, or PhD in Computer Science, Data Science, Geospatial Science, Remote Sensing, or related field.

• Strong proficiency in Python and ML libraries (TensorFlow, PyTorch, Scikit-learn, etc.).

• Experience with LiDAR data processing and tools such as PDAL, PCL, or LAStools.

• Knowledge of GIS tools like QGIS, GDAL, or ArcGIS.

• Hands-on experience in deep learning for image and point cloud analysis.

• Proficiency in working with cloud computing platforms and containerization (Docker, Kubernetes).

• Experience with databases and big data frameworks (PostGIS, Spark, Hadoop) for geospatial data.

• Strong problem-solving and analytical skills with the ability to work independently and as part of a team.

Nice-to-Have’s:

• Experience with 3D reconstruction, SLAM (Simultaneous Localization and Mapping), or sensor fusion techniques.

• Familiarity with OpenCV, Open3D, and similar libraries for 3D data manipulation.

• Knowledge of real-time processing of geospatial data using edge computing.

• Understanding of computer vision techniques for geospatial applications.

Benefits & Perks:

• Competitive salary and performance-based bonuses.

• Health, dental, and vision insurance.

• Opportunities for professional development.