Physical and financial systems are under growing pressure from a changing planet. Neural Earth fuses geospatial intelligence and AI to help organizations understand how environmental, social, and economic factors intersect across space and time. By transforming massive geospatial datasets into clear, actionable insight, Neural Earth enables decision-makers to anticipate risk, strengthen resilience, and plan with confidence—from individual properties to global portfolios.

We are seeking a skilled and inquisitive Geospatial Data Scientist to support the development of advanced geospatial analytics and machine learning capabilities at Neural. This role plays a key part in designing and delivering data products that address critical challenges in environmental monitoring, infrastructure risk, and spatial decision-making. You will collaborate with engineers, scientists, and product managers to build models that process satellite, aerial, and vector data, enabling clients to extract meaningful insights from complex spatial datasets.

Your day-to-day will involve building workflows to clean, analyze, and transform large-scale spatial and temporal datasets. You’ll apply modern statistical and machine learning techniques to detect patterns, model trends, and support predictive analytics. Whether estimating climate impacts or classifying remote sensing images, your work will inform real-world decisions across industries.

This position is ideal for individuals who love to work at the intersection of spatial intelligence and AI, are passionate about open data and tools, and thrive in collaborative, mission-driven environments.

• Architect and train transformer-based models, including BERT, GPT, or vision-language hybrids.
• Build workflows for supervised, unsupervised, and reinforcement learning across NLP and multi-modal tasks.
• Create high-quality datasets with robust labeling/annotation pipelines.
• Fine-tune foundation models for specific use cases (e.g., spatial data parsing, technical document summarization).
• Integrate trained models into production environments via scalable inference services.
• Monitor performance, perform evaluations, and iterate using continuous feedback loops.
• Publish internal documentation and contribute to research outputs where appropriate.
• Work with raster imagery, geospatial data, time series, video, and audio data
• Integrate databases, vector search, data lakes, and streaming data
• Build agentic AI applications for geospatial and edge computing

• Bachelor’s or Master’s degree in Data Science, Remote Sensing, Environmental Science, Geography, or related field.
• 3+ years of experience applying machine learning to geospatial data.
• Proficiency in Python and geospatial libraries (e.g., Rasterio, GDAL, GeoPandas, Scikit-learn, PyTorch, Tensorflow).
• Familiarity with GIS tools like QGIS or ArcGIS
• Experience with Mapbox, Leaflet, and Cesium
• Experience in Python, Python notebooks
• Experience with spatial databases (e.g., PostGIS, DuckDB, Timescale, MongoDB) and cloud-native workflows.
• Experience with graph databases such as ArangoDB, Neo4j a plus
• Experience with search such as ELK stack, Meilisearch, OpenSearch
• Ability to document and articulate architecture diagrams and artifacts
• Excellent written and verbal communication skills.

• Experience with multispectral imagery, SAR data, weather data, or environmental/climate modeling.
• Familiarity with Nodejs, Reactjs, Vuejs
• Familiarity with cloud platforms like AWS or Google Earth Engine.
• Prior contributions to open-source geospatial libraries or peer-reviewed research.
• Knowledge of Docker, Kubernetes, or CI/CD for ML pipelines.

• Up to 10%