Lead Geo-Spatial AI Engineer

Role OverviewWe are seeking a high-energy, curious, and versatile Lead Geospatial AI Engineer to head ourGeoAI R&D team. In this role, you will be the technical architect behind our most complexcomputer vision problems and the operational lead driving multiple projects to success. Youmust be comfortable pivoting between deep research, hands-on coding, and strategic teammanagement in a fast-paced, agile environment.Key ResponsibilitiesTeam Leadership & Mentorship: Lead a cross-functional team of AI engineers; foster aculture of curiosity, continuous learning, and rapid experimentation.Agile Project Execution: Oversee the end-to-end lifecycle of multiple R&D projects, ensuringtimely delivery of prototypes and production-ready models.Advanced R&D: Research and implement state-of-the-art (SOTA) computer visionarchitectures (e.g., Vision Transformers, Diffusion Models, Segment Anything) for diversegeospatial analytics.Scalable AI Pipelines: Design robust MLOps workflows to handle massive multi-modaldatasets (Satellite, SAR, LiDAR, Aerial) from ingestion to deployment.Cross-Functional Collaboration: Partner with product and business leads to translateabstract research into actionable industry solutions for sectors like [ClimateTech/Defense/Urban Planning].Technical Stack RequirementsCore AI & Computer Vision:Frameworks: 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 SegmentAnything (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:Cloud Platforms: Extensive experience with AWS (SageMaker), Google Cloud (Vertex AI), orAzure ML.Pipelines & Versioning: Proficiency in MLflow, DVC (Data Version Control), and Kubeflow.Annotation: Familiarity with high-volume labeling tools like CVAT, Labelbox, or Roboflow.Deployment: Containerization via Docker and orchestration with Kubernetes.Soft Skills & MindsetProblem-Solving Curiosity: A "figure it out" attitude when faced with messy, unstructured, orsparse geospatial data.Radical Flexibility: Ability to shift priorities across projects without sacrificing quality or teammorale.Action-Oriented Leadership: High bias for action; you prefer a working prototype over aperfect theoretical model.Education & ExperienceEducation: BT/MS/PhD in Computer Science, Geoinformatics, or a related quantitative field.Experience: 7+ years in AI/CV, including 2+ years leading technical teams and managingmulti-project portfolios.