Lead Geo-Spatial AI Engineer

Position Overview

We are seeking a high-energy, curious, and versatile Lead Geospatial AI Engineer to head our GeoAI RD team. In this role, you will be the technical architect behind our most complex computer vision problems and the operational lead driving multiple projects to success. You must be comfortable pivoting between deep research, hands-on coding, and strategic team management in a fast-paced, agile environment.

Key Responsibilities

• Team Leadership Mentorship: Lead a cross-functional team of AI engineers; foster a culture of curiosity, continuous learning, and rapid experimentation.
• Agile Project Execution: Oversee the end-to-end lifecycle of multiple RD projects, ensuring timely delivery of prototypes and production-ready models.
• Advanced RD: Research and implement state-of-the-art (SOTA) computer vision architectures (e.g., Vision Transformers, Diffusion Models, Segment Anything) for diverse geospatial analytics.
• Scalable AI Pipelines: Design robust MLOps workflows to handle massive multi-modal datasets (Satellite, SAR, LiDAR, Aerial) from ingestion to deployment.
• Cross-Functional Collaboration: Partner with product and business leads to translate abstract research into actionable industry solutions for sectors like [Climate Tech/Defense/Urban Planning].

Technical Stack Requirements

• Core AI Computer Vision: Mastery of PyTorch (preferred) or TensorFlow.
• CV Libraries: Expert use of OpenCV, TorchVision, SAMGeo, Detectron2, and YOLO variants.
• Advanced Modeling: Experience with Hugging Face Transformers for Vision and Segment Anything (SAM).
• Geospatial Engineering Stack: Processing: Expert proficiency with GDAL/OGR, Rasterio, and GeoPandas.
• Geometry: Deep knowledge of Shapely and Pyproj for CRS management.
• Analysis: Experience with Google Earth Engine, TorchGeo, and QGIS/ArcGIS Pro.
• Data MLOps Infrastructure: Proficiency in MLflow, DVC (Data Version Control), and Kubeflow.
• Cloud Platforms: Extensive experience with AWS (SageMaker), Google Cloud (Vertex AI), or Azure ML.
• Pipelines Versioning: Proficiency in MLflow, DVC (Data Version Control), and Kubeflow.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.